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The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
The Benefits of Adaptive Antennas on Mobile Handsets for 3G ...
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  • 1. The Benefits of Adaptive Antennas on Mobile Handsets for 3G Systems Final Report February 2003
  • 2. 2/83 RA1002/R/17/105/3 This report was commissioned by the Radiocommunications Agency. Copyright © 2003 Multiple Access Communications Ltd Multiple Access Communications Ltd Delta House, Enterprise Road Chilworth Science Park SOUTHAMPTON SO16 7NS, UK Tel: +44 (0)23 8076 7808 Fax: +44 (0)23 8076 0602
  • 3. 3/83 Executive Summary The Benefits of Adaptive Antennas on Mobile Handsets for 3G Systems Final Report Adaptive antenna technology at a cellular base station (BS) has been a subject of interest for many years. With ongoing advancements in the performance of semiconductor technology, chipsets that are both smaller and more powerful are now available for hand-held mobiles. This trend is not expected to slow in the near future and thus the possibility of incorporating smart antenna technology in the handsets seems more possible. In addition there have been recent advances in antenna technology itself that allow small antennas to be located closer together. With this in mind the Radiocommunications Agency (RA) has asked Multiple Access Communications Limited (MAC Ltd) to investigate the current state of the art of smart antenna technology for handsets and the likely performance enhancements they may give to a Universal Mobile Telecommunications System (UMTS) network having the UMTS Terrestrial Radio Access (UTRA) frequency division duplex (FDD) network radio interface. The RA is particularly interested in a quantification of the effect of using smart handset antennas on the following network parameters. • Capacity of the network. • Base station density in the network. • Data rates achievable in the network. • Size of dead zones caused by adjacent channel interference. To begin with, MAC Ltd performed a three-week literature search during which it examined conference and journal papers, contacted experts in the field, searched the internet and spoke to various companies. A principal finding was that the gain in signal-to-interference plus noise ratio (SINR) performance of an adaptive antenna over a single antenna followed a log-normal probability density distribution. Diversity combining techniques achieved 6 to 9 dB gain in SINR, for the 99% reliability level. However, if interference rejection combining was used, the gain in SINR increased to 23 dB in the presence of a single strong interferer. This gain decreased significantly to 16 dB in the presence of two interferers. Multiple input
  • 4. 4/83 multiple output (MIMO) adaptive antenna technology was also considered, but it was concluded that the technology was not currently suited to hand-held mobiles, and neither is it being deployed in 3G BSs. The next step was to formulate a model for an ideal smart antenna in the handset. A statistical model was created for the improvement in SINR based on distribution curves derived from publications. The model was found to be of the log-normal type having mean and standard deviation values shown in Table A. Observe that when the smart antenna is in line-of-sight of the serving sector transmitter, the performance is not as good as when the antenna cannot see the serving sector. Furthermore, as the number of interferers increases, the performance of the smart antenna deteriorates. Line-of-sight Non Line-of-sight Number of Interferers Standard Standard Mean (dB) Mean (dB) Deviation (dB) Deviation (dB) 1 12.5 10.6 22 11.4 2 6.4 9.6 14 10.4 3 6.4 8.6 11.4 9.4 4 or more 6.4 7.6 8.8 8.4 Table A Mean and standard deviation values for the improvement in SINR performance in the presence of interferers. The statistical model of the smart antenna was incorporated into MAC Ltd’s code division multiple access (CMDA) network simulation tool, MACcdma, and simulations were run that compared the performance of the network when the handsets used either a conventional omnidirectional antenna or the smart antenna. Signal coverage was predicted over a 25 km2 area of Central London and simulations were run over a 9 km2 area for different capacities, base station densities and voice/data services. Typical parameters for a 3G network having a UTRA FDD radio interface were used. To investigate the effect of using the smart antenna on the capacity of the network we considered the percentage of the simulation area with a blocking probability that is equal to or less than 2%. This was defined as the figure of merit (FoM). It was found that using the smart antennas increased the capacity of a network by about a third when the FoM was maintained at 95%. As the offered traffic increased, not only did the FoM decrease, but so did
  • 5. 5/83 the improvement in capacity that a smart antenna gave over an omnidirectional antenna. We also considered the effect of using the smart antenna to decrease the number of BSs that are needed for a given capacity and FoM. We found that approximately 8% to 15% fewer BSs were needed. However, this figure should be accepted with caution since we also found that the performance was highly dependent on how well the network had been optimised for co- channel interference. The smart antenna was also found to allow higher data rates on the network. Between 20% and 100% higher data rates could be used with the smart antenna. The improvement was found to be highest for low data rates and to decrease when high data rates are used. Finally, we considered dead zones that are caused by adjacent channel interference (ACI). It was found that smart antennas reduced dead zones to about a third of the size that existed when only an omnidirectional antenna was used in the handsets. However, the reader should be aware that distinguishing between areas of high blocking caused by ACI and areas of high blocking due to co-channel interference is, to some extent, a subjective process. As a further study for the RA we considered the potential of using smart antennas in rural environments to reduce the required number of BSs. In these environments we found that the up link (UL) was the limiting link. Unless the BS also employed some form of adaptive antenna technology there was little benefit in improving the down link (DL) with smart antennas in the handsets. However, if the traffic was heavily DL biased the network may become DL limited and smart antennas in the handset receiver would prove useful to improve the link. In light of the work that has been performed over the course of this project we recommend that the following work be carried out in the future. First, it is suggested that more simulations be run to bring more confidence to the current results and also to allow the trends to be analysed in more detail. Secondly, it would be a useful exercise to investigate the impact of radio resource management on the network. Examining a network in which there is a mixture of users with handsets using omnidirectional antennas and handsets using smart antennas could be very interesting. Finally, we recommend that studies are performed of scenarios in which BS and mobile station (MS) adaptive antennas are combined into one “adaptive” system. Prepared by Multiple Access Communications Ltd February 2003
  • 6. 6/83 Table of Contents List of Abbreviations ............................................................................................................... 10 1 Introduction...................................................................................................................... 13 1.1 Organisation of the Report....................................................................................... 15 2 Literature Search .............................................................................................................. 15 2.1 Diversity Combining................................................................................................ 16 2.2 Beamforming ........................................................................................................... 18 2.3 MIMO technology ................................................................................................... 19 2.4 Recent Developments .............................................................................................. 21 2.4.1 Allgon Mobile Communications.......................................................................... 22 2.4.2 Virginia Polytechnic Institute and State University............................................. 23 2.4.3 University of Surrey Centre for Communication Systems Research................... 24 2.4.4 Philips Research Laboratories, Eindhoven, Holland ........................................... 25 2.5 The interaction of the antenna with the body........................................................... 26 2.6 Conclusions.............................................................................................................. 27 3 Development of a Statistical Model for a Smart Antenna ............................................... 28 3.1 Deriving the Log-Normal Distribution .................................................................... 29 3.1.1 The Non Line-of-Sight Case................................................................................ 29 3.1.2 The Line-of-Sight Case........................................................................................ 36 3.1.3 Number of Interferers .......................................................................................... 37 3.2 Velocity of the Handset ........................................................................................... 39 3.3 Differences Between Wideband and Narrowband Signals ...................................... 40 4 Simulation Procedures and Parameters............................................................................ 40 4.1 Networks and Coverage Predictions........................................................................ 41 4.1.1 Main Network ...................................................................................................... 42 4.1.2 Adjacent Network ................................................................................................ 43 4.1.3 Reduced Networks ............................................................................................... 44
  • 7. 7/83 4.1.4 Uniform Networks ............................................................................................... 44 4.2 Monte Carlo Simulations using MACcdma............................................................. 46 4.2.1 Introduction to MACcdma................................................................................... 46 4.2.2 Modifications to NP WorkPlace .......................................................................... 47 4.2.3 Modifications to MACcdma ................................................................................ 48 4.2.4 MACcdma Parameters ......................................................................................... 49 4.3 Description of Tests ................................................................................................. 49 4.3.1 Increase in Capacity............................................................................................. 50 4.3.2 Decrease in Base Station Density ........................................................................ 50 4.3.3 Increase in Data Rate ........................................................................................... 51 4.3.4 Reduction in Dead Zone Size .............................................................................. 51 5 Simulation Results ........................................................................................................... 52 5.1 Increase in Capacity................................................................................................. 53 5.2 Decrease in Base Station Density ............................................................................ 56 5.3 Increase in Date Rate ............................................................................................... 59 5.4 Reduction in Dead Zone Size .................................................................................. 61 5.5 Rural Link Budget.................................................................................................... 62 6 Summary of Results and Conclusions ............................................................................. 64 References................................................................................................................................ 68 Appendix A MACcdma Parameters ..................................................................................... 72 1 Simulation ........................................................................................................................ 72 1.1 Number of Simulation Snapshots ............................................................................ 72 1.2 Global Traffic Scale Factor...................................................................................... 72 1.3 Output Bin Size........................................................................................................ 72 1.4 Output Statistics Area .............................................................................................. 72 2 Network............................................................................................................................ 72 2.1 Orthogonality Factor................................................................................................ 72
  • 8. 8/83 2.2 Pilot Channel Required Ec/I0.................................................................................... 73 2.3 Attenuation of Extra Interference ............................................................................ 73 2.4 RAKE Efficiency Factor.......................................................................................... 73 2.5 Down Link Noise Figure ......................................................................................... 73 2.6 Down Link Line Loss .............................................................................................. 73 2.7 Up Link Noise Figure .............................................................................................. 73 2.8 Up Link Line Loss ................................................................................................... 73 2.9 Maximum Traffic Channel Power ........................................................................... 74 2.10 Minimum Traffic Channel Power............................................................................ 74 2.11 Relative Pilot Channel Power .................................................................................. 74 2.12 Relative Common Channels Power ......................................................................... 74 2.13 Relative Total Traffic Power ................................................................................... 74 3 Services ............................................................................................................................ 74 3.1 Max MS Transmit Power......................................................................................... 75 3.2 SHO Enabled ........................................................................................................... 76 3.3 Processing Gain ....................................................................................................... 76 3.4 Required Eb/I0 .......................................................................................................... 76 3.5 Channels of this Type .............................................................................................. 76 3.6 Source Activity Factor ............................................................................................. 76 3.7 Transmit Cycle......................................................................................................... 76 3.8 Relative Power ......................................................................................................... 76 4 Call Admission Control ................................................................................................... 76 4.1 Call Admission Control Algorithm.......................................................................... 77 4.2 Down Link Power Headroom .................................................................................. 77 4.3 Maximum Reduction in Up Link............................................................................. 77 5 Soft Handover .................................................................................................................. 77 5.1 Up Link Margin ....................................................................................................... 77
  • 9. 9/83 5.2 Maximum Active Set Size ....................................................................................... 77 5.3 Add/Drop Threshold ................................................................................................ 77 5.4 Add/Drop Hysteresis................................................................................................ 77 5.5 Replacement Hysteresis ........................................................................................... 78 Appendix B Extended Bibliography .................................................................................... 79
  • 10. 10/83 List of Abbreviations 2G Second Generation 3G Third Generation 3GPP Third Generation Partnership Project ACI Adjacent Channel Interference ACLR Adjacent Channel Leakage Ratio BLAST Bell Laboratories Layered Space-Time BER Bit Error Rate BS Base Station CAC Call Admission Control CCSR Centre for Communication Systems Research CDF Cumulative Distribution Function CDMA Code Division Multiple Access CPICH Common Pilot Channel DECT Digital European Cordless Telephone DL Down Link EAG Effective Antenna Gain EGC Equal Gain Combining ETSI European Telecommunications Standards Institute FDD Frequency Division Duplex FoM Figure of Merit GSM Global System for Mobile Communications IRC Interference Rejection Combining IQHA Intelligent Quadrifilar Helical Antenna LOS Line of Sight MAC Ltd Multiple Access Communications Limited MEG Mean Effective Gain
  • 11. 11/83 MIMO Multiple Input Multiple Output MPRG Mobile Portable Radio Research Group MRC Maximal Ratio Combining MS Mobile Station NLOS Non Line of Sight PDF Probability Density Function QoS Quality of Service RA Radiocommunications Agency R&D Research and Development RF Radio Frequency SAR Specific Absorption Rate SC Selection Combining SDMA Space Division Multiple Access SHO Soft Handover SINR Signal-to-Interference plus Noise Ratio SIR Signal-to-Interference Ratio SMS Short Message Services SNR Signal-to-Noise Ratio STC Space-Time Coding TDD Time Division Duplex TDMA Time Division Multiple Access UK United Kingdom UL Up Link UMTS Universal Mobile Telecommunications System UTRA UMTS Terrestrial Radio Access VCE Virtual Centre of Excellence VTAG Virginia Tech Antenna Group
  • 12. 12/83 VLSI Very Large Scale Integration VTVT Virginia Tech VLSI Telecommunications WCDMA Wideband Code Division Multiple Access WTEC World Technology Evaluation Centre
  • 13. 13/83 1 Introduction Commercially viable third generation (3G) networks are conspicuous by their absence in Europe, in spite of the first European Telecommunications Standards Institute (ETSI) / Third Generation Partnership Project (3GPP) specification issue, known as Release 99, in December 1999. Since then we have had Release 4 in March 2001, and currently we are on Release 5, issued in December 2001. The operators spent vast amounts of money on acquiring the 3G licences, and are now faced with the huge costs of purchasing the 3G equipment and deploying it. Nevertheless, the networks are being installed and the first 3G network in the United Kingdom (UK) is expected to be operational in the next few months. The 3G networks will be radically different from the previous second generation (2G) ones. Instead of being focused on circuit-switched voice and short message services (SMS), 3G will have, in addition, multimedia services. 3G will accommodate both circuit-switched and packet data services, with transmission rates that in principle may be 2 Mbps, although the maximum rate is more likely to be 384 kbps or only 128 kbps. Both symmetrical and asymmetrical transmissions will be supported. The radio access method in 3G is based on code division multiple access (CDMA), rather than the time division multiple access (TDMA) used in the Global System for Mobile Communications (GSM). Therefore, the interference conditions in 3G networks are radically different from those in GSM networks. In 3G we have intracellular interference from users in their own cell, a situation that does not occur in GSM. Further, as all cells may use the same carrier frequency, there is intercellular interference from all cells. Much research has been directed to decreasing the intracellular interference, eg, by using multi-user detection; and for mitigating the effects of intercellular interference, eg, by using adaptive antennas at base stations (BSs) that, in addition to tracking the wanted signal, are able to steer nulls in the antenna pattern towards the interfering signals. The greater the amount of interference that can be removed, the more users that can be accommodated for the same service. For example, for a single cell and using multi-user intracellular cancellation methods, it has been shown [1] that, in the presence of multiple users, the performance for any user can be made the same as if only one user was present. So the quest to decrease interference in CDMA systems is worthwhile as it results in huge performance gains in 3G networks. The radio spectrum in the UK is regulated by the Radiocommunications Agency (RA). The RA requires the spectrum to be used efficiently and effectively for the benefit of the nation,
  • 14. 14/83 and accordingly spectral efficiency for good service provision is one of its key aspirations. While the RA waits for 3G networks to become operational in the UK, it needs to know which future technologies might result in major enhancements in spectral efficiency in 3G networks. Such enhancements will manifest themselves by providing: (1) a significant increase in the teletraffic carried for a given network spectrum allocation and BS density; (2) a decrease in the number of BS sites for a given teletraffic and spectrum allocation; (3) an increase in user bit rate; (4) a decrease in the number and size of dead zones due to adjacent channel interference; and so on. One technology emerging from current research and development (R&D) activities that seems capable of achieving these enhancements is adaptive antenna technology for handsets. While diversity, beam switching, and beamforming with interference cancellation have been used at BSs, where antenna size and spacing, as well as processing power, are not critical, introducing such techniques into the small handsets we have today is a daunting problem. This is because the physical size of current handsets is smaller than a person’s hand, and as the 3G Universal Mobile Telecommunications System (UMTS) frequency division duplex (FDD) band is from 1920 to 2170 MHz, the half wavelength, and hence the separation required between the antenna elements, is some seven centimetres. Therefore, the ability to deploy multiple antennas within the restricted space of a small handset while ensuring that the signal fading on each antenna is essentially uncorrelated is a very difficult task. Conventionally, the spacing between multiple antennas is usually greater than half a wavelength, and to pack antennas closer together than this requires novel concepts to ensure that the signal correlation between the received signals on each antenna is low. In October 2002 the RA issued an invitation to organisations to tender for a four-month investigation into the benefits of adaptive antennas in mobile handsets for UMTS systems. The RA required that the investigation commenced with a short literature search to identify the current state of R&D in adaptive antenna technology for small 3G handsets. Next, on the assumption that such handsets will exist in the future and will be universally deployed, the RA wanted to know the increase in UMTS FDD network performance that would accrue compared to when conventional omnidirectional antennas are used. Network performance parameters of specific interest to the RA were the capacity, BS density, user bit rate, and the dead zones due to the interference from an adjacent network.
  • 15. 15/83 In our tender to the RA we proposed the following methodology. From the findings of a literature search we would derive a statistical model for the signal-to-interference ratio (SIR) of a handset having an adaptive time-varying antenna pattern to one having the conventional fixed omnidirectional antenna pattern. The adaptive antenna would attempt to maximise the SIR, ie, it would minimise the interference as well as tracking the wanted signal. This model of the SIRs would then be integrated into the Multiple Access Communications Limited (MAC Ltd) 3G simulator, called MACcdma. The UMTS simulations would then be run for an area of Central London, and the improvements in network performance ascertained for speech users only, 64 kbps data users only, and 144 kbps data users only. MAC Ltd was awarded the contract, and what follows is the result of our investigation. 1.1 Organisation of the Report The first task was a literature search, which included contacting international experts in the field, as well as having discussions with companies who are in the business of making relevant antennas. Section 2 describes these findings, and enables us in Section 3 to propose a statistical model of a handset having a smart antenna that can adapt to changes in mobile location and the number of interfering BS transmissions. We emphasise that the smart antenna is assumed to have an impact on the down link (DL) only, ie, the BS to mobile station (MS) link, and that the BS is considered to have a single antenna. Section 4 gives a detailed description of our simulation procedures; specifically, it addresses how our UMTS planning tools are modified to facilitate the statistical model we have derived for the handset, and then describes the network parameters and services to be used in the simulation. Section 5 gives the results of the simulations, and their analysis. The final chapter discusses our key findings, and identifies further work that is necessary to achieve a greater understanding of the value of this technology for handsets as 3G networks mature. 2 Literature Search There has been much interest in deploying smart antennas at the BSs in cellular networks to track up link (UL) transmissions from roaming handsets within their cells, while at the same time rejecting intercellular interference from mobiles in neighbouring cells [2]. By contrast comparatively little research and development has been done on incorporating smart antenna technology into the tiny handsets that are currently in use. Indeed, the problem of introducing multiple antennas into the confines of a handset, and being able to optimise the antenna pattern with the movements of a user in the signal scattering environment that characterises
  • 16. 16/83 the handset environment is not for the faint-hearted. Fortunately, there are always a few who will address difficult problems if the potential gains in network performance are sufficiently enticing. At the current time a few companies and research organisations [3][4][5] have experimental results that encourage us to believe that the smart antenna handset will be realisable and available in the future. The RA has asked MAC Ltd to place emphasis on identifying the performance enhancements of using smart antennas on hand-held mobile phones as opposed to larger mobile phones. This is because hand-held phones are expected to be the principal mobile equipment used. The term, smart antenna, refers to signal processing performed on the received or transmitted signals at an array of antennas. This processing yields a resulting signal that has an enhanced quality when compared to the signal associated with a single antenna. There are three types of systems that use multiple antenna arrays: diversity combining, beamforming, and multiple input multiple output (MIMO). Experiments involving diversity combining were reported as early as 1927 [6]. Adaptive beamforming was developed in the 1960s for sonar and radar [7] and it was not until the early 1980s that the application of beamforming for cellular systems was seriously considered [8]. MIMO technology is the latest and most disruptive of technologies; ‘disruptive’ because the MIMO technology offers the prospects of huge gains in bits per second per Hertz, a revolutionary step forward in spectrum provisioning. Much is owed to the pioneering work of Winters [8] and the conceptual work of Foschini [9]. Provision for MIMO technology has been made in the 3G specification, although it is unlikely that the 3G networks will use it for some years because of its complexity. We will now briefly discuss each of these multi-antenna technologies. 2.1 Diversity Combining In this report we are concerned with the DL in mobile cellular systems. Multipath fading on the DL is caused by the transmitted signal from the BS following multiple paths to the receive antenna [6]. The signals emerging from each path normally have different amplitudes, phases and polarisations such that when vectorially added together at the receiver the resultant received signal is characterised by rapid changes with time and receiver position. We may consider the antenna patterns of the individual antennas to be time-invariant and their received signals to have a low correlation. Diversity combining exploits the differences in the received signals, due to multipath, at two or more antennas to mitigate fading and improve the overall quality of the signal. For example, for a two antenna switched diversity
  • 17. 17/83 reception combiner, the best signal is selected. The probability of both signals at the receive antennas being in a deep fade at the same time is small compared to one of the received signals being in a fade. For higher order switched diversity, when there are many antennas, there is always a high probability that one of the received signals is not faded. In this situation the fading channel, after diversity reception, resembles a Gaussian channel [10]. This gives a diversity gain, defined as the reduction in the required average input signal-to-interference plus noise ratio (SINR) at each antenna for a given bit error rate (BER) with fading. There are three principal forms of diversity: spatial, polarisation, and angle (or pattern) diversity [11]. Spatial diversity has been mentioned above. Placing the antennas sufficiently far apart in space enables the combiner to exploit the fact that the received signals at the antennas are essentially uncorrelated. Polarisation diversity is somewhat limited as there are only two orthogonal polarisations, and therefore it is limited to second-order diversity. Again the fading of the signals on each antenna is required to be uncorrelated. In angle diversity there is a set of antennas pointing in different directions, and in the scattering environments found in cellular radio the signals on the different antennas will exhibit different fading characteristics. Notice that if we have only one receiver antenna the result is fading that will have a deleterious effect on performance, but if we have multiple antennas and arrange them such that the fading on each antenna is independent, then we thank nature for providing signals in numerous forms, which allows us to extract a single signal when the fading is minimal. The signals received at each of the antenna elements can be combined in different ways to mitigate the effects of fading [12]. We referred above to the simplest diversity combining method, selection combining (SC), where the strongest signal is chosen from the array of antenna outputs. A superior, but more complex, method is maximal ratio combining (MRC). Here, the first step is to cophase the signals at the output of each antenna, and then the signals are weighted proportionately to their individual SINRs. This results in the individual SINRs being summed to give a maximised SINR. A simpler version of MRC, called equal gain combining (EGC), gives all of the input signals the same weighting before summing the individual SINRs. Traditionally, the accepted antenna spacing between the elements of an antenna array is between a half and one wavelength. The optimum spacing between antenna elements in a two-element broadside linear array is about 0.7 λ [13], where λ is the wavelength of the signal. This increases to about 0.8 λ for a four-element array. The UMTS Terrestrial Radio
  • 18. 18/83 Access (UTRA) FDD DL frequency band is 2110 - 2170 MHz in the UK. This corresponds to a wavelength of 14 cm. At this frequency range a two-element broadside linear array needs a separation of about 10 cm and is too wide to fit into a small handset. However, Winters [11] points out that as a handset is typically surrounded by scatterers, antenna separations as small as λ/4 will still enable the signals at the antennas to have a low correlation. This means that, at the UTRA FDD frequencies, antenna separations of less than 4 cm can be used. Private correspondence with experts in the field has suggested that antennas could be spaced as close together as an eighth of a wavelength. We observe that in these classical diversity schemes the interference is not explicitly handled, nor is the required signal explicitly catered for. The idea is that the antennas will receive the desired signal plus noise and interference, and given this situation, the combiner will attempt to maximise the SINR. There is no feedback to the antennas to change their antenna patterns to yield further improvements. Steering antenna beams in the direction of the wanted signal, while forming nulls in the antenna pattern to essentially ignore the interfering signals comes under the category of beamforming techniques; a subject we will address next. 2.2 Beamforming Perhaps the simplest method of providing a narrow beam to a receiver is to use an array of antennas, in which each antenna has a fixed narrow beam pointing in a specific direction. For example, there may be 12 radial beams each covering a sector of 30 degrees, and a handset uses the beam serving the sector in which it is positioned. Because of the narrowness of the sectors the interference is, in general, decreased. This type of arrangement is referred to as a switched antenna system and is usually deployed at a BS [14]. Beamforming arrays can be deployed at a BS to provide multiple beams, where each beam independently tracks different roaming handset receivers. These arrays can be linear, circular, or planar [15]. Usually half wavelength spaced antenna elements create the spatially selective beams that allow multiple user signals to be supported within the same bandwidth at the same time. More advanced systems adjust their antenna radiation pattern formed from a set of antennas to steer beams at the strong multipath signals of the wanted signal, and steer nulls at the significant interferers, and in doing so optimise the SINR. For N antenna elements, the receiver can effectively combat N-1 interferers [11]. In these systems the antenna pattern must be continually modified as the user roams in order to optimise the performance. In principle these antennas
  • 19. 19/83 can be used in the BSs and in the handsets. However, at present they are used exclusively in the BS domain, but we will be considering their application in handsets. 2.3 MIMO technology In recent years a great deal of research has gone into developing multiple input multiple output antenna systems, in which different data may be transmitted from each of the multiple antennas. There are numerous types of these systems with radically different aims, and we will refer to all of them as MIMO systems. Some people attach a specific definition to MIMO, one that achieves a high data rate per user, ie, the so-called Bell Laboratories Layered Space-Time (BLAST) technology from Lucent. However, using our definition, one type of MIMO system is space-time coding (STC). Here the receiver antenna elements are spaced sufficiently far apart to ensure that the fading on each element is statistically independent. There are two basic types: space-time block codes [16], and space-time trellis codes [17]. Both provide transmit diversity and sometimes receiver diversity, enhancing the data integrity without increasing a user’s data throughput. As a simple example of space-time block codes, consider the case of two antennas at the BS transmitter and two receiver antennas at the handset. Instead of transmitting the same data on both antennas we may transmit different data on each antenna. Since both transmissions will arrive at the two receiver antennas we need to be able to untangle the two sets of data. We can do this using the method proposed by Alamouti [16]. In this method we create a frame having two time slots, and we have data x and data y to transmit. For the first time slot we form the complex conjugate of minus the data x, namely -xc, and transmit this from antenna A1, while at the same time we transmit data y from antenna A2. In the next time slot we transmit from these two antennas x and +yc, respectively. On the provisos that we sound the two channels to get their impulse responses, and that the channels are not too dispersive, we can recover both x and y. Although the data rate has not increased, we have used both time and space diversity to significantly improve data integrity. Another MIMO system is space division multiple access (SDMA) [18]. Consider the case of two mobiles, each having one antenna, while the BS has two antennas. The transmissions from the two mobiles travel via different paths as they are in different locations, and as far as the BS is concerned it is dealing with four different channel impulse responses, two from each of the two mobiles. Armed with accurate estimates of these channel impulse responses, the signals from both mobiles, transmitting in the same band at the same time, can be
  • 20. 20/83 determined. SDMA has the ability in this example to double the throughput and improve data integrity. MIMO systems are able to increase dramatically a user’s data rate to such an extent that it represents a fundamental improvement in spectral efficiency. This concept was postulated by Foschini [19], and is known as BLAST. In conventional diversity systems the Shannon capacity of a system grows with the log of the number of antennas used [9]. In BLAST, if there are M transmit antennas and N receive antennas that have independently fading signals, the capacity of the system grows linearly (rather than logarithmically) with the smallest number of antennas, min(M,N) [19]. This represents a vast improvement over traditional smart antennas. For example, Lucent has demonstrated that a transmission rate of 1.2 Mbps can be realised in 30 kHz using eight transmit and 12 receive antennas in an indoor environment [2]. This corresponds to 40 bits per second per Hertz. Higher efficiencies have been reported, although the efficiency is dependent on there being many radio paths, ie, on the richness of the multipath environment. There are several problems that face the BLAST technology [20]. Much of the research has assumed perfectly uncorrelated channel models whereas, in reality, signals at different receiver antennas will be partially correlated. MIMO systems do not yield high values of bits per second per Hertz if correlation between the antennas’ received signals is too high. The capacity gains described above also assume that the complexity of the signal processing required is acceptable. In practice there often has to be a trade-off between complexity and performance. There is also the problem of antenna separation at the handset. To maximise the data throughput the antennas need to be sufficiently separated to ensure low cross-correlation between the received signals. This is difficult to achieve on a small handset at the frequencies used for 3G. However, it has been estimated that MIMO will work at an antenna spacing of 0.25 to 0.3 of a wavelength [21]. At 2 GHz the wavelength is 15 cm, requiring an antenna separation of about 5 cm. There is speculation that, due to the scattering of the signals around the body or head, 1/8 λ may be possible. Gesbert, Ekman and Christophersen [22] have considered placing antennas within an array one wavelength wide. Their work showed that six antennas provided a capacity about 6.5 times greater than that provided by just one antenna. Increasing the number of antennas further does not give further improvement. This antenna is still considered to be too wide for a hand-held mobile phone.
  • 21. 21/83 In this literature search we did not find any recent work that advocated BLAST for small hand-held antennas. Measurement campaigns using BLAST have been performed in New York City but these were for at least five or more antennas at the transmitter and the receiver [23][24][25]. However, based on the potential capacity gains, future research may prove exceedingly fruitful, and Lucent has proposed that its BLAST technology be used for 4G. The work of Lucent Technologies, Stanford University and Iospan Wireless (now owned by Intel) should be followed closely as they are all actively engaged in developing this technology. 2.4 Recent Developments Using smart antenna technology in the handset traditionally has not been considered feasible, but a report by the World Technology Evaluation Centre (WTEC) highlights the work that various companies are currently pursuing [3]. Philips engineers observed that 50% of the power consumed in the handset is due to the radio frequency (RF) electronics. To conserve battery life and cost while changing from the simple, single antenna to a smart antenna array requires that the cost and signal processing power per antenna must significantly decrease. The company is also interested in dual polarisation diversity, but because the diversity order is only two, other forms of diversity will still be required. ATR, an antenna company based in Japan, is investigating the integration of antennas into the RF electronics chip. Problems with antenna gain are expected when they do this, and the effect of the hand on the terminal is of concern. Nokia is studying multiple antennas in handsets and combating the effect of the hand on the multiple antenna pattern, perhaps by using only those antennas not affected by the presence of the hand, or by compensating for it by adjusting the antenna impedance. Nearly every company the WTEC visited is doing significant research on smart antenna technology, although most of the effort is still for adaptive multiple antennas to be used at the BSs, rather than in the handsets. What appears to be a universal opinion is that smart antenna technology is necessary for the enhancement of future wireless cellular networks. It appears that companies and research organisations are beginning to overcome the basic concerns of size, cost and power consumption that multiple antennas in handsets present. Current research and development focus on using smart antennas in handset receivers to maximise the signal quality on the DL. Specifically, the handset receiver is required to optimise the SINR. The UTRA FDD common pilot channel (CPICH) may be conveniently employed to characterise the forward channel and thus produce a better adaptive array [26].
  • 22. 22/83 In FDD transmissions the handset transmitter does not know the current characteristics of the UL propagation environment, and therefore the handset transmitter does not know the optimum transmission configuration to use for its smart antenna array (should it have one). This problem is overcome if the time division duplex (TDD) version of UMTS is used. Although companies are researching the use of smart antenna technology in handsets, it has been difficult to access information directly from these companies. Most of the published material we have acquired has been obtained directly from universities or industry-university partnership consortia, or through the traditional sources of journal and conference papers. The web was another source of high-level information. We also contacted known experts in the field by email and telephone to gain their insights and opinions on the current state of smart technology for handsets. Several companies were also contacted directly for their views, and although their opinions often contained confidences that we cannot reveal, the discussions were nevertheless helpful. We would like to thank Dr David James of ArrayCom for visiting us and giving us a stimulating lecture on his company’s adaptive array technology; Dr Reinaldo Valenzuela of Lucent Technologies for comments and articles; Dr Arogyaswami Paulraj of Stanford University for his information; Dr Simon Saunders of the University of Surrey for a bibliography; and Professor Jørgen Bach Anderson of Aalborg University, Denmark, for his comments and references. Finally, we would like to thank Erling Erlingsson and Colin Ribton from Antenova for an informative description of their small antenna technology and for providing a useful bibliography. We will now detail the main avenues that were pursued in the literature search. 2.4.1 Allgon Mobile Communications Allgon Mobile Communications is a company based in Sweden that is collaborating with the Hong Kong University of Science and Technology in the field of adaptive antenna technology for mobile handsets. They have investigated the use of two handsets having two- branch antenna systems: one had a quarter-wave monopole and a shorted patch antenna; while the other employed a monopole and a planar antenna. Two methodologies were used, the first being a computer simulation and the second using a measurement process. In their first methodology the antenna patterns were measured and used in a computer model of the antennas. A theoretical model of the incoming multipath scattered signals was also created. In the simulation the antenna radiation patterns were moved through the incoming
  • 23. 23/83 signals. The signals received at the antennas were then stored and further processed to determine the performance of the interference rejection combining (IRC), EGC, and SC algorithms. Their second technique was experimental, employing the two handsets in an indoor environment and in the presence of a phantom head and hand. Different measurement routes were used to simulate the effect of signals coming from different transmitters and the received complex antenna signals were sampled as the mobiles, with their phantoms, traversed these routes. The propagation frequency used was 1805 MHz [27]. The mobile receivers were not in line-of-sight (LOS) of the transmitter, and the cross-correlation of the received signals at the two antennas was low. The received signals at the antennas were then processed off-line in non real time to identify the performance of the three diversity algorithms. In both the simulation and measurement techniques the signals were weighted to achieve a mean SIR of 15 dB at the antennas in the presence of a single interferer. The measurements were repeated using two interferers where the first interferer was kept at 15 dB below the carrier signal power at the handset, whilst the second interferer was set to be 20 dB below the carrier signal power. By these means data were acquired and used to evaluate the performance of diversity in the presence of one or two interferers. The main results revealed a diversity gain of 7 dB and 9 dB at the 99% reliability level when SC and EGC, respectively, were used in the presence of a single interferer. This means that for 99% of the time the SIR due to SC was 7 to 9 dB better than that due to a single antenna. However, the diversity gain increased to 23 dB for IRC. In the presence of the second interferer, SC and EGC performed about the same as with a single interferer, but IRC now gave a diversity gain of 16 dB. 2.4.2 Virginia Polytechnic Institute and State University The Virginia Polytechnic Institute and State University in the USA has been investigating smart antennas. Dietrich [12] has compared different diversity techniques using two-antenna arrays. Propagation measurements were performed using a 2.05 GHz unmodulated carrier wave, and diversity gains of 7 to 9 dB for the 99% reliability level were reported. Dietrich has also expanded on the work of Braun et al. [27] to achieve diversity gains greater than 20 dB in the presence of a single interferer.
  • 24. 24/83 Sponsored by the US Navy and Texas Instruments, the Mobile Portable Radio Research Group (MPRG) and the Virginia Tech Antenna Group (VTAG) [28] performed handset transmit diversity measurements for an indoor channel at 2.05 GHz. A 5 MHz bandwidth carrier was used with binary phase shift keying modulation. In a LOS environment diversity gains of 2 to 7 dB were found for a 99% reliability level. This gain increased to 10 dB for non line-of-sight (NLOS) conditions. Although these measurements are promising, we will not include transmit diversity in the UL in our investigations. This is because transmit diversity needs a feedback loop in the measurement campaign. Closed loop mode transmit diversity is supported at the BS of a UTRA FDD system [29], but does not appear to be supported at the mobile. The Virginia Tech Very Large Scale Integration (VLSI) for Telecommunications Laboratory (VTVT) has performed simulations for both diversity combining and adaptive combining in a simulated UTRA FDD network [26][30][31]. Two antennas, separated by a quarter of a wavelength, were assumed. The interference from another adjacent channel was also modelled at the receiver. In these simulations there was an improvement of up to approximately 5.5 dB relative to that of a single omnidirectional antenna. However, further study is needed to investigate the impact of specific model parameters on the results. This work was done in conjunction with LG Electronics. 2.4.3 University of Surrey Centre for Communication Systems Research The Centre for Communication Systems Research (CCSR) at the University of Surrey has been developing an intelligent quadrifilar helical antenna (IQHA) [32]. The work was performed in conjunction with Nokia Mobile Phones, UK, and the Mobile Virtual Centre of Excellence (VCE). This dual-band handset antenna deploys adaptive technologies that can switch between a hemispherical pattern for satellite communications and a toroidal pattern for terrestrial communications. SC, MRC and EGC diversity schemes were compared. In a LOS environment the diversity gain was negligible [33], eg, the diversity gain for the 99% reliability level was approximately 0.5 dB using EGC. The performance was better in a NLOS environment as the mean diversity gain increased to about 13 dB for EGC. Further improvements may be anticipated as the research is continuing.
  • 25. 25/83 2.4.4 Philips Research Laboratories, Eindhoven, Holland Dolmans and Leyton from the Philips Research Laboratories, Eindhoven, Netherlands, have developed an adaptive dual antenna handset for the Digital European Cordless Telecommunication (DECT) system for use in indoor environments [34]. EGC diversity was used with two antennas, and in the NLOS case they reported a diversity gain of 9 dB with a 99% reliability level. 2.4.5 Antenova Limited Antenova Limited, an antenna company, is developing a new generation of antennas with high dielectric, smaller size, and higher efficiency than conventional antennas, and with an immunity to detuning while having directional and steerable properties [5][35]. The RA considered the work of Antenova to be significant to this project and asked MAC Ltd to have discussions with the company. Accordingly MAC Ltd had a meeting with Antenova on 8th November 2002, when Antenova informed us that it is able to produce isolated antennas that can be positioned within millimetres of each other. These antennas are able to form beams with a nominal beamwidth of about 80 degrees. At the time of writing Antenova was expected to release imminently a dual antenna wideband CDMA (WCDMA) demonstrator with an anticipated gain over conventional antennas of about 6 dB for the 99% reliability level. Antenova’s antennas have also been used in trials with Innovics Wireless’ Trailblazer product that has recently been announced [4]. This has an anticipated diversity gain of 7 dB over conventional receivers, for a 99% reliability level. It was interesting to note that Antenova had performed a literature search similar to this one and they observed that much of the research into adaptive antennas for handsets reveals that, for diversity combining using a dual antenna system, a gain of about 6 to 9 dB over a conventional antenna for the 99% reliability level is realised. These references are included in the extended bibliography at the end of the report. Antenova found just two research programs that achieved greater gains. These were the work of Braun and Dietrich that we have already discussed in Sections 2.4.1 and 2.4.2. These gains are slightly lower than what is theoretically expected. Saunders [36] concludes that for the 99% reliability level case, gains of about 10 dB are achievable when two uncorrelated, equal mean power Rayleigh faded signals are combined using SC. EGC and MRC are slightly better by about one and two decibels, respectively.
  • 26. 26/83 2.5 The Interaction of the Antenna with the Body We are all familiar with the sensitivity of the received signal to small movements of the handset, a condition caused by the radically different phase and amplitude of the received multipath components in a scattering environment. In addition there are the significant changes that can occur in the received signal power between people, the effect of people on the radio environment, whether a person wears glasses, and so on. Pederson et al. [37] investigated this variation of the mean effective gain (MEG) for 200 people receiving GSM1800 signals in an indoor environment. The handsets had a retractable three-quarter wavelength whip, a retractable helical antenna, and a back-mounted patch antenna. Their main findings are that the variation in MEG between people can be up to 10 dB; the difference between the absence and presence of a person’s head is some 10 dB for a helical antenna, 6 dB for a whip antenna, and 3 dB for the directive patch antenna; the effect of a person’s height and whether they wear glasses is small; and there was an effect depending whether a person is right- or left-handed. Arai et al. [38] studied the relative antenna gain as a function of handset size and a person’s size in indoor and outdoor environments. They found that the amount the antenna protrudes above the head, which is a function of a person’s size and antenna type, resulted in a gain variation of some 3 dB between users. Scanlon and Evans [39] have investigated body-worn antennas that can have their efficiency degraded by the body absorbing power and causing changes to the radiation pattern. It is interesting to note that the body interacts with electromagnetic energy as a lossy dielectric, decreasing the wavelength of the propagating wave. High water content tissues, like blood and muscle, are more absorptive than fat. There will be less loss if the antenna is further from the body, placing the antenna in a jacket instead of a shirt pocket saves about 4 dB. The influence of the body is greater at 3G frequencies than at GSM frequencies, and it is advisable that there is sufficient spacing between the antenna and the body to decrease body attenuation losses. Nevertheless, radiation pattern fragmentation will occur. And there are many other factors that add to received signal sensitivity. For example, Flomerics [40] has found that for Bluetooth, a plastic enclosure can attenuate a RF signal by up to 37%, and further, the tuning can be shifted out of the Bluetooth frequency range. Designers know how to accommodate these effects, but it is another problem they face.
  • 27. 27/83 Indeed, as Morishita et al. point out [41], designers take into consideration the conducting material of the handset case near the antenna as part of the antenna radiator, as well as the loss of performance due to the proximity of the body. The antenna design must also mitigate the specific absorption rate (SAR), particularly into the head. As counteracting measures we may anticipate the antenna structure to be software controlled to optimise the SAR, the signal loss due to body absorption, and the frequency de-tuning by the body. Another prudent approach is that advocated by Leisten and Rosenberger of Sarantel [42] in whcih the antennas are dielectrically loaded to control the resonance of their near fields and to use a feed topology that isolates the antenna from the handset ground. This makes the performance of the antenna more predictable, and independent of the presence of the hand and other parts of the body. Antenova has also opted for high dielectric antennas, as mentioned previously. The use of dielectric loading antennas is a feature we may expect to be used extensively in future small handsets. All of the above is not, with exception of Antenova, for the complex case of multiple adaptive antennas in a handset, but for the current conventional ones. When we start to consider these body effects in smart antennas the problems are exacerbated. The significance of the findings detailed above is that they emphasise that solutions to the problem we face in this report are analytically intractable, and that statistical approaches have to be used as there are just too many unknowns. We may anticipate that two people having the same handset with an adaptive array in the same place may receive significantly different signals, even if the scattering environment is unchanged due to the absence of other people and vehicles. 2.6 Conclusions In the course of this three week literature search we have identified some of the main players and their research in the area of smart antennas for 3G handsets. We have discussed some of these developments but the reader may wish to consult the references directly and the extensive bibliography found in Appendix B is provided for further information. We found that diversity combining appears to be achieving about 7 to 9 dB diversity gain when two antennas are used in a handset. It has been harder to find research related to adaptive array antenna technology for small mobile handsets. This does not necessarily mean that no research is underway, but companies are reluctant, for commercial reasons, to disclose their research programmes. Of the two sources that were found, there was common
  • 28. 28/83 agreement that overall antenna gains in the order of 20 dB were achievable using interference rejection in an adaptive antenna array. MIMO systems were found to be a very active research area, but the interest seems to be focused on its use at BSs; or on large terminals, such as laptop computers with wireless interfaces, rather than on small handsets. We note that for MIMO technology to be used, with or without handsets, the BSs would need to deploy multiple antennas and transmit different data from each antenna. MIMO technology therefore represents a quantum step in complexity, but is a technology for the future. The RA is currently more interested in discovering the gains that would accrue using adaptive antennas in the handset when the first set of UMTS BSs are deployed. These BSs are most likely to transmit using one antenna, so it was agreed with the RA that MIMO technology would not be included in our simulation studies. Although the literature was not found to be well endowed with relevant data, we found sufficient information to formulate a statistical model of a smart antenna that represents the gain in the SINR that might be realisable using handsets with adaptive antennas instead of the conventional single antenna. We will now report on the model we used in our studies. 3 Development of a Statistical Model for a Smart Antenna Conventional antennas on mobile handsets are omnidirectional in the horizontal plane and, when vertical dipoles or monopoles are used, have vertical polarisation [43]. We will assume that the gain towards the BS from these antennas is always at 0 dBi. Smart antennas, however, try to optimise the SINR performance for a given environment. This means they have an effective gain over traditional omnidirectional antennas. It is not possible to use a ‘typical’ antenna pattern of a smart antenna because the pattern is constantly changing depending on the environment and presence of interferers. Neither can one easily predict the antenna pattern at a given time because the direction of arrival of all of the incoming carrier and interfering signals needs to be known. It is practically impossible to model this in a simulation because even a ray-tracing model will not correctly account for all signal paths. The alternative to using an accurate time-varying antenna pattern is to use a statistical model of the antenna gain. We will derive a probability density function (PDF) for the effective antenna gain (EAG), in which the EAG is the gain in decibels of the required signal that results from combining the signals from the elements of the antenna array in a smart way.
  • 29. 29/83 3.1 Deriving the Log-Normal Distribution From the literature it is reasonable to assume that the SINR performance of a single antenna approximately follows a log-normal distribution. This is also true for the SINR performance of the combined signal at the output of an adaptive antenna array. The EAG at a given point in space is the difference between the SINR of the adaptive array output and the SINR from the single antennas. By the central limit theorem the statistical distribution of the EAG will also be log-normally distributed. From Kreyszig [44] we see that the mean of a sum of random variables equals the sum of the means. Similarly, the mean of the difference between two random variables will be the difference between their means. The variance of the sum of independent random variables is given by the sum of the variances of these variables [44]. Given the normal distributions of the single antenna and the adaptive antenna, we can use the above two properties to calculate the mean and the variance of the EAG. The standard deviation of the distribution can then be derived from the variance. 3.1.1 The Non Line-of-Sight Case We saw in Section 2.4.1 that Allgon Mobile Communications has produced good results when interference rejection combining (IRC) is used in an indoor environment. Figure 1 shows the cumulative distribution function (CDF) of the SINR recorded after various combining techniques were implemented in the case of one interferer. The SINRs achieved from the individual antennas, A and B, are also shown, where Antenna A was a quarter wave monopole and Antenna B was a shorted patch antenna on a printed circuit board. For example, Antenna A gave a SINR better than about −24 dB for 99% of the time. This is highlighted by the filled circle in Figure 1. Implementing selection combining (SC) or equal gain combining (EGC) clearly gives an improvement in the performance of the SINR. For example, when SC is used the SINR is better than about −17 dB for 99% of the time, highlighted by the unfilled solid circle in Figure 1. This is a 7 dB improvement over Antenna A. IRC clearly surpasses the diversity combining techniques, with a SINR of about 0 dB as shown by the unfilled dashed circle in Figure 1. This is 24 dB greater than that obtained using Antenna A. In Figure 2 we show the equivalent results when two interferers are used. Notice that the IRC technique does not perform so well. The 99% reliability level SINR is now only −10 dB.
  • 30. 30/83 Figure 1 CDF of output SINR in the presence of one interferer that is 15 dB below the signal power. From Figure 4 (left) of Braun et al. [27].
  • 31. 31/83 Figure 2 CDF of output SINR in the presence of two interferers. The first interferer is 15 dB below the signal power. The second interferer is 20 dB below the signal power. From Figure 4 (right) of Braun et al. [27]. The distributions given in Figure 1 and Figure 2 were reproduced in Microsoft Excel in order to derive their means and standard deviations. These values are given in Table 1 for Antenna A, Antenna B, and the IRC SINR output. These means and standard deviations were then used to plot CDFs of the log-normal distributions. One can see from Figure 3 that log-normal distributions, ie, normal distributions when the variable is expressed in decibels, are good approximations of the measurement data and our assumption made above is valid.
  • 32. 32/83 One Interferer Two Interferers (SIR = 15 dB) (SIR = 15, 20 dB) Standard Standard Mean Mean Deviation Deviation Antenna A and -3 10.4 -5 9.1 B SINR (dB) IRC SINR (dB) 18 7.8 8 8.2 EAG (dB) 21 13 13 12.3 Table 1 The mean and standard deviation of the output SINRs and the EAG in the presence of one or two interferers. Based on Braun et al. [27]. 100% 90% 80% 70% 60% CDF 50% 40% 30% 20% 10% 0% -30 -20 -10 0 10 20 SINRout (dB) Antenna A Antenna B IRC Log-Normal CDF of Antenna A and B Log-Normal CDF of IRC Figure 3 The CDF of output SINR in the presence of two interferers has been reproduced based on Figure 4 (right) from Braun et al. [27]. Log-normal distributions have been superimposed using the output SINR mean and standard deviation given in Table 1.
  • 33. 33/83 From the central limit theorem the difference between the two normal distributions, the EAG, will produce a third normal distribution. The mean of the new distribution, µ, is given by, µ = µ1 − µ 2 , (1) where µ1 and µ2 are the means of the original distributions. The new standard deviation, σ, is found by summing the variances of the two distributions, ie, σ= (σ 1 )2 + (σ 2 )2 , (2) where σ1 and σ2 are the original standard deviations. Table 1 shows the mean and standard deviation of the resulting log-normal distribution of the EAG. We can see that the mean EAG is 21 dB and the standard deviation is 13 dB when there is only one interferer, but if two interferers are present the mean gain decreases by 8 dB to 13 dB. At 12.3 dB the standard deviation has changed little from before. In a later paper Braun et al. [45] compared the EAGs for different environments. They found that for one prototype handset the diversity gain was about 1 dB less in an urban environment than in an indoor environment. A second prototype’s diversity gain was 0.3 dB worse in the urban environment. The diversity gain performance in an urban environment is not as good as an indoor environment because there tends to be less scattering in an urban environment. The signals outside are more correlated and diversity combining has less of an effect. We will assume that the worst case example is true for all cases and that urban environments have a mean EAG that is 1 dB less than the indoor case, ie, 21 - 1 = 20 dB in the one interferer case and 13 - 1 = 12 dB in the case of two interferers. Dietrich [12] noted that the work of Braun et al. required a priori knowledge of the desired signal, which was used as a reference signal. The uncorrupted desired signal was available in the reported experiments but is not available in practice. Dietrich went on to produce the results of an adaptive beamforming technology for a hand-held antenna array consisting of four antenna elements. Measurements were made in an indoor NLOS environment using a phantom head and hand at 2.05 GHz. Figure 4 shows the CDF of the SINR performance of the individual antennas (Ch1-4) and the improved performance due to beamforming in the presence of a single interferer. Note that log-normally distributed CDFs match the data well.
  • 34. 34/83 100% 90% 80% 70% 60% CDF 50% 40% 30% 20% 10% 0% -30 -20 -10 0 10 20 30 40 SINRout (dB) Ch1 Ch2 Ch3 Ch4 IRC Log-Normal CDF of Chs1-4 Log-Normal CDF of IRC Figure 4 The CDF of output SINR in the presence of one interferer has been reproduced based on Figure 9-2 from Dietrich [12]. Log-normal distributions have been superimposed using the output SINR mean and standard deviations. Using Equations 1 and 2 the mean and standard deviation of the average antenna output SINR and the IRC output SINR were used to calculate the mean and standard deviation of the EAG for the Dietrich adaptive antenna. It was found that the EAG has a mean of 25 dB and a standard deviation of 9 dB. The mean compares well with the 21 dB mean EAG found by Braun et al. The standard deviation is smaller than Braun’s 13 dB. Dietrich extended the measurement programme to a microcellular environment in which it was found that the mean SINR improvement, ie, EAG, for a hand-held in different outdoor environments was 11.9 dB. This is low compared to Dietrich’s indoor result of 25 dB. Even if we were to use Braun’s 1 dB offset for urban environments we would expect Dietrich’s outdoor mean to be closer to 25 − 1 = 24 dB. Dietrich suggests that his outdoor mean values are poor because the signal-to-noise ratio (SNR) is dominating over the SINR when the
  • 35. 35/83 transmitter powers are only 27 dBm, and thus the smart antenna does not perform so well. In a real UTRA FDD network we may expect transmit powers of the order of 43 dBm [46] and the SNR may not be so bad. For this reason we will assume that the actual EAG performance in an urban environment, according to Dietrich, is 24 dB. We will now assume that the overall urban NLOS log-normal distribution is characterised with a mean and variance that is the statistical average of the distributions obtained from Braun and Dietrich. It can be shown that the mean value, µA,B, in a statistical distribution is given by, n A ⋅ µ A + nB ⋅ µ B µ A, B = , (3) n A + nB where nA and nB are the number of samples from two distributions A and B, and µA, and µB, are the means of the individual distributions, respectively. If we assume that the number of samples in each distribution is the same, then the overall mean, µ, is, 20 + 24 µ= = 22 dB. (4) 2 Similarly, it can be shown that the variance of the combination of two distributions is given by, varA, B = ( 2 ) ( n A ⋅ varA + µ A + n B ⋅ varB + µ B 2 ) − µ A, B , 2 (5) n A + nB where varA,B is the overall variance and varA and varB are the variances of the individual distributions, A and B, respectively. Using Equation 5, and assuming that nA = nB, the standard deviation,σ, of the combined distribution is, 132 + 20 2 + 9 2 + 24 2 σ= − 22 2 = 11.4 dB. (6) 2 In conclusion we can state that the log-normal distribution of the EAG in the NLOS case has a mean of 22 dB and a standard deviation of 11.4 dB in the presence of one significant interferer.
  • 36. 36/83 3.1.2 The Line-of-Sight Case It is expected that smart antennas will behave differently in a LOS environment than in a NLOS environment as there will be less multipath in the former case and the dominant signal will exhibit smaller angular spread. Dietrich [12] performed measurements in an urban LOS environment. Measurements were taken by the receiver as it was moved over two different paths. For each of the two paths two array configurations were used. In the co-polarised array configuration the individual antenna elements in the array all had the same polarisation, whereas multi-polarised array configurations had different polarisations for the individual antenna elements. The measurements were taken with the transmitter in two different locations. Table 2 shows the results for the eight sets of measurements. The mean SINR gain is the difference between the mean SINR when the signals from the antenna elements are combined and the mean SINR of the signals received at the individual antenna elements. The one percent SINR gain is the difference between the one percent cumulative probability SINR when the signals from the antenna elements are combined and the one percent cumulative probability SINR of the signals received at the individual antenna elements. Using these results we have estimated the standard deviation of the log-normal distribution, which is also shown in Table 2. Observe that the mean and standard deviations varied considerably from one measurement path to another. Transmitter A Transmitter B 1% Estimated 1% Estimated Mean Mean Array SINR Standard SINR Standard Path SINR SINR Configuration gain Deviation gain Deviation gain (dB) gain (dB) (dB) (dB) (dB) (dB) 1 co-polarised 20.2 25.7 2.4 4.8 32.1 11.7 multi- 1 17.9 27.0 3.9 4.9 23.1 7.8 polarised 2 co-polarised 4.5 25.7 9.1 21.1 32.1 4.7 multi- 2 5.1 27.7 9.7 21.1 28.8 3.3 polarised Table 2 Results of peer-to-peer hand-held measurements, from Dietrich [12].
  • 37. 37/83 Using Equation 3 (extended to eight data sample sets) the overall mean is given by, 20.2 + 17.9 + 4.5 + 5.1 + 4.8 + 4.9 + 21.1 + 21.1 µ= = 12.5 dB. (7) 8 Notice that the mean EAG is significantly lower in the LOS case than in the NLOS case. Similarly, using an extended version of Equation 5, the overall standard deviation is 10.6 dB. This is similar to the NLOS value. 3.1.3 Number of Interferers In Table 1 we saw that the EAG was different depending on the number of significant interferers that were present. The standard deviation dropped by only about 1 dB but the mean EAG in the presence of two interferers was 8 dB less than when there was only one interferer. Unfortunately, we found no reports in our literature search on the effect of more than two interferers on the performance of the smart antennas. As there is little literature on the performance of handset smart antennas in the presence of multiple interferers, we need to make some assumptions about the effect of multiple interferers based on what we actually know. In an urban environment the cell density is usually sufficiently high that there will always be at least one significant interfering BS at each mobile. Other interferers will only be significant if they are of comparable strength to the first interferer. If the other interferers have a low power, relative to the strongest interferer, then the smart antenna will focus on nulling the strongest interferer and ignore the others. However, if the other interferers are of a comparable strength to the strongest interferer, the performance of the smart antenna will deteriorate. The antenna now needs to suppress two interference sources. We will define a significant interferer as any interferer that is within 8 dB of the strongest interferer. Allgon has shown that a separation of 5 dB between two interference levels significantly reduces the performance of the smart antenna. We have added a 3 dB margin to this value because we expect interferers that are even weaker than 5 dB from the strongest interferer to still harmfully effect the performance of the adaptive antenna. The total number of nulls or beams that can be steered in an M element array is M−1 [11]. In a real network the number of significant interferers will vary depending on the precise location of the handset. Figure 5 gives an indication of the number of significant interferers within 8 dB of the strongest interferer in a typical dense urban network.
  • 38. 38/83 Figure 5 Number of interferers in a snapshot of a typical dense urban network. The plot has been created using MACcdma and displayed in MAC Ltd’s network coverage planning tool, the NP WorkPlace. One can see that there are no areas with zero interferers or just one significant interferer. There is one interferer within 8 dB of the strongest interferer, ie, a total of two interferers, within a small portion of the map area. These areas are shown in green. A larger area of the map, shown in yellow, has three interferers. The remaining map area, in orange, shows where there are a total of four or more interferers. In these cases we assume that the number of interferers is greater than the number of nulls that can be steered. The smart antenna is swamped by too many interferers and, given an array of only four elements, it cannot adapt well enough. We will assume that the smart antenna is also performing maximal ratio combining (MRC) and continually comparing the SINR performance (or bit error rate performance) provided by the MRC and IRC techniques. When IRC performs badly, the smart antenna can resort to the MRC approach. The performance gain of MRC remains fairly constant independently of the number of interferers because MRC does not try to reject interferers. Rather, Rayleigh fading due to multipath is being overcome. Dietrich performed measurements for diversity gain using MRC in LOS and NLOS environments. He found [12] that the 99% reliability level using two antennas was
  • 39. 39/83 6.4 dB and 8.8 dB in the LOS and NLOS case, respectively. Dietrich did not provide the mean or the standard deviation of the diversity gain. Neither did he provide plots. Since we are not able to reproduce the precise distribution we will assume that the mean diversity gain is equivalent to the diversity gain at the 99% reliability level. We will assume that the standard deviation continues to decrease by 1 dB with each additional interferer. No literature could be found on the effect of three interferers on the performance of the smart antenna. In this case we will assume that the smart antenna’s performance is halfway between that of two interferers and four or more interferers. Table 3 summarises the means and standard deviations that will be used in the statistical model. These values will be used in the MACcdma simulation described in Section 4. Note that if we assume an 8 dB drop in the performance of the adaptive antenna in a LOS environment when the number of interferers increases from one to two, we would get a mean EAG of 4.5 dB. Since this is below the performance of MRC we will assume that MRC is used and the mean EAG is 6.4 dB. Line-of-sight Non Line-of-sight Number of Interferers Standard Standard Mean (dB) Mean (dB) Deviation (dB) Deviation (dB) 1 12.5 10.6 22 11.4 2 6.4 9.6 14 10.4 3 6.4 8.6 11.4 9.4 4 or more 6.4 7.6 8.8 8.4 Table 3 Mean and standard deviation for the EAG in the presence of different numbers of interferers. 3.2 Velocity of the Handset The VTVT Laboratory investigated the performance of diversity combining and adaptive combining at various velocities in a WCDMA simulation [31]. They concluded that there was no degradation in performance with increasing velocity of the mobile when a diversity combining technique was used with two antennas [26]. However, they also found that the performance of an adaptive antenna improved slightly as the handset velocity decreased [30]. There was about a 1 dB improvement in the required SIR level for a BER of 5% when the mobile’s velocity decreased from 30 km/hr to 2 km/hr. This improvement increased to about 3 dB for a BER of 2%. In urban environments, where vehicles do not tend to drive at high
  • 40. 40/83 speeds, we would expect the mobile velocity to vary between walking speed and about 70 km/hr. MACcdma does not model the velocity of individual handsets so we will assume that they are all stationary or at walking speed, thus the EAGs given in Table 3 can be used. Although greater speeds may experience a small degradation in the performance of the smart antenna, it is expected that future designs will perform better and be able to perform fast enough to respond to the rapidly changing environment. 3.3 Differences between Wideband and Narrowband Signals The measurements performed by both Braun and Dietrich used narrowband signals, whereas UTRA FDD uses a wideband signal of 5 MHz. Dietrich suggests that the performance of an antenna array in wideband systems would be the same for most systems when the delay spread is less than the chip or symbol period [12]. However, if the delay spread is too large, the receiver will not be able to cope because the delay spread causes the symbols to merge into one another. In this case, the adaptive antenna will be unable to choose the optimum weights for the individual antenna elements. A RAKE receiver at the UTRA FDD receive end performs diversity combining of the delayed paths to help remove this inter-symbol interference, in addition to despreading the received signals. However, it does not try to suppress interferers. We will assume that the EAG is the same value at 5 MHz as it is for a narrowband carrier wave. Little has been published in this area but future wideband measurement programs at Virginia Tech should reveal the true story. 4 Simulation Procedures and Parameters MAC Ltd has developed a WCDMA simulation tool, known as MACcdma, that allows a network planner to investigate the quality of a WCDMA network given a particular radio coverage plan and traffic profile. Before MACcdma can be used we must first create a cellular network and generate signal strength coverage predictions in the area of concern. This was performed using MAC Ltd’s proprietary radio planning tool, the NP Work Place and we begin this section with a description of this process. Next, MACcdma was used to assess the effect of using a smart antenna in a WCDMA network as opposed to a standard omnidirectional antenna. In order to generate useful results we devised a realistic network model using performance metrics and system specifications that are expected to exist in a real network. Various 3G technologies have been specified but we focused on the UTRA FDD system since the UK operators will be deploying this technology. Finally, we go on to highlight the details of the particular simulations that were run. Specifically, we discuss
  • 41. 41/83 parameters that have been varied to identify the performance enhancements of using the smart antennas in the 3G network over conventional omnidirectional antennas. 4.1 Networks and Coverage Predictions MAC Ltd’s NP Work Place allows sectors to be positioned on a digital map. Predictions can then be performed to identify the signal strength from the sectors at various location bins around the sector. The benefits of smart antennas are of most interest in urban areas, since this is where the network will experience the greatest load and the spectrum utilisation will be at its highest. For this reason, and in agreement with the RA, we chose a 5 km × 5 km area of London as the basis for our simulations. Figure 6 shows a building and vegetation plot from the NP Work Place showing the area of concern, where the buildings are coloured grey and the vegetation (trees and shrubs rather than mere grass) is green. Note the position of the River Thames in the bottom right region of the map. Hyde Park is the big expanse in the middle left of the map. Figure 6 A 5 km × 5 km square in the centre of London.
  • 42. 42/83 4.1.1 Main Network Once we had chosen the location of the environment under investigation it was necessary to design the layout of the main network. One important factor is the density of the network as this will determine the relative impact of the DL or the UL on the overall performance of the network. We have assumed that the smart antenna will only provide a benefit on the DL so the difference between the two links is important. We assumed that a typical UTRA FDD network will have a site density similar to a typical 2G GSM network. Within one square kilometre we have used a total of 20 sectors, four of which are three-sectored macrocells and the remaining eight being individual microcells. The sectors were initially positioned on a regular hexagonal grid. Each individual sector was then repositioned to ensure that it was on top of a suitable building if it was a macrocell, or in a street if it was a microcell. We can see these sites in Figure 7. There were a total of 105 three-sectored macrocell sites and 226 microcell sites in the 5 km × 5 km simulation area. Note that we have removed, or moved, sites that were in the middle of parks or the River Thames. It is unlikely that a network operator would be able to obtain planning permission to build BSs on the statues in Hyde Park!
  • 43. 43/83 Figure 7 The sites as they were positioned on the London map. Suitable antennas were chosen for the two site types. The macrocells used three, 65-degree beamwidth antennas, while the microcells used dipoles. Predictions were then run using MAC Ltd’s radio propagation predictors, MiniWorks and MicroWorks, respectively. Ignoring park and river sites, we predicted coverage for 93 three-sectored sites and 196 microcell sites. This is a total of 475 sectors (or cells) in the 25 km2 area. We have based these site densities on our understanding of site densities that are currently used by 2G network operators in dense urban networks. 4.1.2 Adjacent Network As part of this study the RA asked MAC Ltd to investigate the effect of adjacent channel interference (ACI) on the UTRA FDD network performance and what benefits smart antennas might realise over omnidirectional antennas in the presence of ACI. To do this we also created an adjacent network. This was done using a method similar to that described above for creating the main network. The sites were placed on a different hexagonal grid from that of the main network, except for co-located sites that were positioned at the same position as the main network’s sites. In agreement with the RA we have assumed that
  • 44. 44/83 approximately 14% of the sites in a typical 3G network will be co-located. In the adjacent network we have used a total of 95 three-sectored sites (14 of which are co-located) and 187 microcell sites (25 of which are co-located). This is consistent with the level of site sharing that currently exists in the UK. 4.1.3 Reduced Networks The RA was interested to quantify the effect of using a smart antenna on the number of BSs that are needed to support a given level of traffic. To do this we modified the main network by removing selected sites and simulating the “reduced” network. The number of sites that were removed is shown in Table 4; we repeated the simulations for these various site densities. This enabled us to establish trends in the quality of service (QoS) as the BS density was decreased. The removed sites were distributed evenly around the whole network area to help ensure a reasonably uniform network. Percentage of Number of Number of Total Number Main Network Macrocell Sites Microcell Sites of Sectors Remaining (%) Removed Removed Removed 100 0 0 0 95 5 11 26 90 11 23 56 80 21 45 108 70 32 68 164 60 42 90 216 50 53 113 272 Table 4 The number of sites that were removed from the main network. 4.1.4 Uniform Networks In a perfect network we would assume that as the BS density decreased the QoS would always decrease in a uniform manner due to the overall reduction of down link power in the network. However, the main network is based on a “pseudo-real” network and it has not been optimised for the best performance, ie, there will be some fluctuation in the performance of a network with a given network density depending on the specific location of the sites. As the number of sites is reduced, the performance of the network will deviate slightly from the
  • 45. 45/83 expected general trend. This is because the interference environment in the network is changing. For example, removing a sector may reduce the interference to other sites in the region, improving the network performance in that region. To overcome the problem of using a non-perfect network we also considered a uniform hexagonal network. In this uniform network we assumed there were no buildings and we used only three-sectored macrocells. Figure 8 shows an example of the uniform network where 30 sites are used. The distance between the sites was scaled up and down to change the number of sites within the 25 km2 area in a predictable manner and simulations were generated from the various scaled networks. Figure 8 The uniform hexagonal network with 30 sites.
  • 46. 46/83 4.2 Monte Carlo Simulations using MACcdma As already discussed, the simulations were run in an area of London that is 5 km × 5 km large. Using the NP Work Place, we predicted the signal strengths of various macrocellular and microcellular sites in this 25 km2 area. These coverage plots were then used by MACcdma to simulate the UTRA FDD network using omnidirectional antennas in the handsets. The simulations were repeated assuming that smart antennas are used by each mobile. 4.2.1 Introduction to MACcdma MACcdma is basically a Monte Carlo simulator for 3G CDMA network planning. Many snapshots of coverage and other parameters are required to achieve sufficient statistical validity; random fluctuations in the network performance are averaged out over successive snapshots. Although the coverage predictions were made over a 25 km2 area, simulations were run only over an area that is 3 km × 3 km as simulations over the full 5 km × 5 km area will not be valid at the edges of the map. In a real network mobiles at the edge of the map will experience interference from BSs beyond the range of the map. This was not modelled in the simulation as we do not have coverage predictions from BSs outside the map region. In addition, mobiles at the edge of the map may even be better served by BSs off the edge of the map. This edge area is represented as the green area in Figure 9. A similar edge effect occurs for the results of the simulation. The performance of the mobiles at the edge of the 3 km × 3 km square will not be valid because we do not model interference from mobiles outside of this square. This edge area is the blue region in Figure 9. A 1 × 1 km square in the centre of the map, highlighted in pink, was chosen from which to extract the simulation results.
  • 47. 47/83 Figure 9 Specific areas within the simulation process. Edge effects due to a lack of coverage predictions occur in the green area. Edge effects due to a lack of simulated mobiles occur in the blue region. The red region shows where the simulation results are valid. In order to compare the QoS of different networks we will principally consider the blocking performance, which is the percentage of attempted calls that are not allowed on to the network. The call admission control (CAC) algorithm determines whether or not a call is allowed on to the network, and the decision is based on the impact of the extra interference due to the new user on the current users on the network. We will also look at the dropped call performance to ensure that this is at an acceptable level. A dropped call occurs when a connection to the network is terminated because the network can no longer support that call. It is also important to verify that sufficient pilot quality exists throughout the simulation area. 4.2.2 Modifications to NP WorkPlace Currently MACcdma assumes that all mobiles have omnidirectional antenna radiation patterns and so the LOS/NLOS property of the environment is not relevant. But, as we saw in Section 3, the smart antenna performs differently depending on whether or not it is in LOS of the serving BS. Therefore, we need to model LOS in our simulations. To do this we began by assuming that a mobile in any bin in the prediction area was served by the strongest server, ie, the sector that had the highest signal strength at that bin. In practice this will not always be
  • 48. 48/83 true since uneven loading of the sectors means that they will experience different levels of interference and the strongest server will not always be the actual server for a particular mobile. However, we will see later that we use an even traffic density across the 25 km2 area of concern. This means that in most cases in our specific simulations the strongest server will be the actual serving sector. The NP Work Place was modified to produce a LOS coverage file. For every 5 m bin in the coverage area, the LOS coverage file indicates whether or not a mobile at that location is in LOS of its strongest server. This LOS coverage file was then imported into and used by MACcdma. 4.2.3 Modifications to MACcdma MACcdma required several modifications to include the effect of the smart antennas. First of all, it had to be able to read in the LOS coverage file. When the MACcdma simulation is run, mobiles are placed randomly throughout the simulation area. MACcdma then identifies whether or not each mobile is in LOS with its strongest server by reading from the LOS coverage file. This determines which log-normal distribution to use for the smart antenna gain over a standard antenna. MACcdma’s standard antenna has a gain of 0 dBi. We saw in Section 3 that the smart antenna will have an improved SINR performance over a single antenna and defined this as the effective antenna gain, EAG. The EAG is log-normally distributed around the mean and in order to model a smart antenna, functions were written to calculate the smart antenna’s EAG using a log-normal distribution, given the mean and standard deviation that are taken from a lookup table. This function has several parameters passed to it. Firstly, a LOS flag is passed to indicate whether or not the mobile is in LOS of its server. Secondly, the number of interferers is passed. This number is obtained from the interference list that MACcdma already produces. If there is an adjacent network we also consider the interference this network produces in the main network. The smart antenna gain function selects the appropriate gain and standard deviation from those given in Table 3 and randomly selects an antenna gain from a truncated log-normal distribution. A truncated log-normal distribution will not allow EAGs that are smaller than EAGmin or larger than EAGmax, where, EAG min = µ − 2 ⋅ σ , (8) and,
  • 49. 49/83 EAG max = µ + 2 ⋅ σ , (9) where µ is the mean EAG and σ is the standard deviation. Truncating the log-normal distribution to data within two times the standard deviation from the mean allows us to include 95% of the complete log-normal distribution whilst avoiding unreasonably small or large EAGs. In addition to the two standard deviation truncating, we also do not allow the EAG to drop below zero. This is because we have assumed that the smart antenna will always do better than a single antenna. Note that smart antennas, based on current research reviewed in the literature search, only perform in the DL. This is because the antenna cannot predict the UL channel in a FDD system. For this reason we have assumed smart antennas behave as if there was no gain due to the smart antenna on the UL and set the mean EAG to zero with a standard deviation of 0. This is effectively a constant antenna gain of 0 dBi. 4.2.4 MACcdma Parameters Before running MACcdma one must define various parameters that govern the specific UTRA FDD network that will be simulated. These parameters are quite extensive and so we have included them in Appendix A for reference. What we can say here is that we have chosen parameter values that we believe are appropriate for a typical UTRA FDD network. The offered traffic is modelled as Erlangs per square kilometre in MACcdma. In agreement with the RA this traffic was uniformly distributed across the whole of the simulation area, including parks and open areas, such as in Hyde Park. 4.3 Description of Tests The RA has asked MAC Ltd to provide graphs that show the (1) increase in network capacity, (2) decrease in number of BSs, (3) improvement in data rates and (4) reduction in dead zone size. To do this we ran several MACcdma simulations using the networks described in Section 4.1. The basic procedure was to run a few test simulations to see what kind of traffic levels gave reasonable QoS before homing in on the traffic levels with which we were most concerned to produce meaningful and useful results. In the following sections we will discuss in detail the methodology for gathering the four principal outputs requested by the RA.
  • 50. 50/83 4.3.1 Increase in Capacity Smart antennas can increase the SINR in the DL. This means that a hand-held mobile is less susceptible to interference and noise. In an urban environment the cells are relatively small and so the limiting factor is not the noise floor but the interference due to other mobiles. But, since each mobile is now able to overcome more interference, the network can support more mobiles in each cell. The purpose of the first set of simulations was to identify the effect of using a smart antenna on the capacity of the network. As far as possible we have tried to use the scientific method, ie, controlling our variables such that only one is changed at a time. For example, we do not consider service mixes as there may be a different effect from using the smart antenna on the different services. Instead, we assumed the only service being used is voice at 12.2 kbps. The parameters for this service are detailed in Appendix A. Using the main network described in Section 4.1.1 and the standard omnidirectional antenna in the handset, the offered traffic was uniformly scaled up across the whole of the simulation area while noting the QoS. This process was repeated using the smart antenna. The offered traffic for the two antennas could then be compared for a given QoS. 4.3.2 Decrease in Base Station Density As an alternative to providing a higher capacity, the use of smart antennas may enable the network operator to use fewer BSs for a given capacity. Voice traffic was again assumed. We began with the main network described in Section 4.1.1 along with the omnidirectional handset antenna. Keeping the traffic level constant, the number of sites was reduced, as described in Section 4.1.3, and re-simulated. This was repeated using the smart antenna in the handsets, but maintaining the same traffic level. These simulations enabled us to estimate the relative reduction in BS density that can be achieved from using smart antennas over normal antennas in the handsets, for a given offered traffic level. As we discussed in Section 4.1.4 it was also necessary to consider the performance of a uniform hexagonal network. The density of this network was changed by reducing the site separation and simulations were run on the various networks.
  • 51. 51/83 4.3.3 Increase in Data Rate A network operator may also want to exploit smart antennas to provide greater data rates. As before we used the main network described in Section 4.1.1. Various data rates were considered and these are detailed in Appendix A. The simulations were repeated with the smart antenna using the same offered traffic and data rates. We were then able to find the improvement in data rate that can be achieved for a given QoS when the smart antenna is used instead of the omnidirectional antenna. 4.3.4 Reduction in Dead Zone Size Ideally, a perfect UTRA FDD transmitter would contain all of its transmit power within its allocated 5 MHz band. However, in a real transmitter, power spills outside of the 5 MHz band because power amplifiers and transmit filters do not have a perfect signal cut-off at the edge of the band. The mobile receiver is also unable to reject all of the power received in an adjacent channel. The adjacent channel leakage power ratio (ACLR) is defined in the 3GPP specification [47] as the ratio of the transmitted power to the power measured in an adjacent channel. The ACLR is 33 dB for an adjacent carrier that is separated by 5 MHz from the signal carrier and we have used this value in our simulations. The dead zone is an area around the interfering BS within which mobiles are blocked by the ACI from that site. Dead zones tend to exist in areas that are far from the serving cell but close to the interfering site. Dead zones in the UL may also exist since the mobile is transmitting at full power to reach its serving cell. This power may be strong enough to cause significant interference at the adjacent channel site. Assuming all of the mobile handsets are using omnidirectional antennas we began by simulating the adjacent channel network, described in Section 4.1.2. MACcdma has the ability to export a DL power map. This is a file containing the total DL power falling in each bin of the simulation area. This power map was imported into the simulation of the “victim” network, ie, the network suffering from the adjacent channel interference. Note that the interference powers will be attenuated by the ACLR of 33 dB to account for the fact they are not in the same band as the interfered network. Identifying the reduction in size of dead zones due to the smart antenna is difficult. This is because we need to distinguish between an improvement in the network QoS due to the smart antenna suppressing ACI and improvements that would exist irrespective of the presence of
  • 52. 52/83 ACI. To simplify matters slightly we have assumed that potential dead zones only exist within a 50 metre radius of the adjacent sites. These are illustrated in Figure 10. Using the main network with no ACI we established a level of traffic low enough not to cause significant QoS degradation within the circular areas under investigation. Using the smart antenna in these areas will not cause a significant improvement in the QoS because it is already good. The simulations were repeated, but this time the ACI was included. The QoS can be compared before and after using the smart antenna with the confidence that the improvement is due to the removal of dead zones. Figure 10 Potential dead zone areas. The pink circles are centred around the black adjacent channel sites. Sites in the victim network are shown in blue. 5 Simulation Results To understand and quantify the improvement in the network when smart antennas are used in the handsets rather than omnidirectional antennas we have run several simulations. There are many parameters that could be varied in the simulations and, as far as possible, we have tried to control the variables by only changing one feature at a time. This allows us to have a better understanding of the individual variables. The principal variables we have changed include the traffic density, the network site density, the type of handset antenna (smart or omnidirectional) and the existence of a network using an adjacent channel. In this section we
  • 53. 53/83 present the main results. We also detail any specific procedures that were used to obtain these results. In order to compare quantitatively the QoS of the different networks in which the handsets use either omnidirectional or smart antennas, we have used a figure of merit (FoM). This is defined as the percentage of the simulation area with less than or equal to 2% blocking. For example, a FoM of 95% means that 95% of the simulation area has a blocking probability less than or equal to 2%. This FoM would represent a network having a good service coverage. This FoM reflects the fact that the blocking probability experienced by a user is dependent on the location of the user. 5.1 Increase in Capacity Co-channel interference can potentially degrade the performance of a 3G network in either the DL or the UL. However, in a dense urban network mobiles tend to be close to their serving sector and each mobile will have plenty of power to overcome the UL interference. The DL is not as favourable because the BS power must be shared between all the mobiles. This makes the DL the limiting link in an urban environment. Any improvement that can be made to this link will improve the overall performance of the network. It was hypothesised that if the smart antenna can significantly reduce the DL interference received from co- channel sectors then the capacity of the network will increase. In this section we show the results of simulations that support this hypothesis. Figure 11 shows the simulation results for the main network. Notice that, for this particular network, when the offered traffic is about 150 Erlangs per square kilometre the FoM is 95% when the omnidirectional antenna is used (blue line). This means that at this traffic level the QoS is good. When we use the smart antenna (magenta line) the offered traffic increases to about 200 Erlangs per square kilometre for the same QoS. This means that the capacity of the network increases by about one third when a smart antenna is used instead of an omnidirectional antenna. One can see that as the traffic increases the smart antenna is less effective in improving the overall capacity of the network. As the network becomes more fully loaded the BSs have to transmit at ever-increasing power to support their traffic. It is suggested that the sharp decline in FoM, observed in Figure 11, is due to the fact that the BS reaches a point at which it can no longer increase its transmit power.
  • 54. 54/83 100% 98% 96% 94% 92% FoM omni 90% smart 88% 86% 84% 82% 80% 0 50 100 150 200 250 300 350 2 Traffic (Erlangs/km ) Figure 11 The FoM for different offered traffic levels when the smart and omnidirectional antennas are used. Figure 12 shows the same data that are presented in Figure 11, but plotted slightly differently. On the x-axis is the offered traffic when the omnidirectional antenna is used. The y-axis gives the percentage increase in offered traffic achievable using the smart antenna and when the FoM is maintained the same as for the omnidirectional antenna. We saw above that for this network, 150 Erlangs per square kilometre is an optimal level to achieve a good FoM. The smart antenna gives about 33% gain in traffic at this level. As the traffic increases the benefit of using a smart antenna in the handsets falls to zero. This is expected to be linked to the limit on the total transmit power available to the transmitter at the BS. However, this region of the curve is of less interest to a real network operator as they are expected to be operating at traffic levels significantly lower than the pole capacity, ie, they will build their networks to operate with surplus BS transmit power to ensure an acceptable QoS.
  • 55. 55/83 90% 80% 70% 60% Gain in Traffic 50% 40% 30% 20% 10% 0% 60 80 100 120 140 160 180 200 220 240 260 280 300 2 Omni Traffic (Erlangs/km ) Figure 12 The gain in traffic achieved when a smart antenna is used instead of an omnidirectional antenna. The black line is a linear regression of the data. The simulations were run using 100 snapshots. To help ensure that the results are statistically valid we also ran a few simulations using more snapshots. Table 5 shows a comparison between the results achieved using 100 snapshots and the results when more snapshots are used for a particular network. As one can see, the difference is negligible and we can be confident in the statistical validity of using 100 snapshots.
  • 56. 56/83 Handset Traffic Density Number of FoM (%) Antenna (Er/km2) Snapshots Omnidirectional 70 100 95.5 Omnidirectional 70 200 95.5 Omnidirectional 80 100 94.9 Omnidirectional 80 200 94.8 Omnidirectional 100 100 93.5 Omnidirectional 100 200 93.5 Omnidirectional 100 300 93.5 Smart 70 100 97.5 Smart 70 200 97.5 Smart 80 100 97.3 Smart 80 200 97.3 Table 5 The FoM for different numbers of snapshots. 5.2 Decrease in Base Station Density An alternative method of quantifying the benefit of using smart antennas instead of omnidirectional antennas is to find the reduction in the site density of the network that can be achieved through the use of smart antennas for a given traffic density. The capacity of a CDMA network is limited by the noise and interference in the system. Since the handsets with adaptive antennas are capable of rejecting more co-channel interference we would expect that fewer sites are required to overcome this noise. We began with the main network given in Section 4.1.1 and removed sites in a uniform manner as described in Section 4.1.3. Figure 13 shows the results when simulations were run with a traffic density of 200 Erlangs per square kilometre. As we expect, the general trend is for the FoM to fall with the BS density. A high traffic level is used since the trend is not so clear at lower traffic levels. We can also see that the smart antenna performance (pink line) is better than the omnidirectional antenna (blue line) since the FoM is consistently higher. In Section 4.1.4 we introduced the idea that the specific network configuration will make a big difference in the overall performance of the network. This is clearly seen in Figure 13. The reader is particularly drawn to the large jump in FoM between the 100% and 95% BS density levels. Observation of the specific network found that a three-sectored macrocellular site was removed from the main simulation area causing a large coverage hole and this is the cause of this large change
  • 57. 57/83 in FoM. Note also that the two curves converge as the BS density is reduced. At these densities the network is saturated with traffic and we saw in Section 5.1 that when this happens the smart antenna is no longer beneficial to the network performance. 100% 95% 90% 85% FoM omni 80% smart 75% 70% 65% 60% 40% 50% 60% 70% 80% 90% 100% 110% Relative Number of Base Stations Figure 13 The FoM when the smart antenna is used in the main network. The traffic density is fixed at 200 Er/km2. We can estimate the improvement in FoM of the network, when using smart antennas instead of omnidirectional antennas, by finding the difference between the FoM at each network density. These are shown in Table 6. We will ignore the 50% and 60% BS density improvements because at these densities the network is overloaded and the smart antenna’s performance is converging with the omnidirectional antenna’s performance. Network operators are unlikely to use such low BS densities. This means that the minimum improvement in the FoM, due to the smart antenna being used instead of the omnidirectional antenna, is 1.9%. The maximum is 6.5%.
  • 58. 58/83 Relative Number % Area with Blocking <= 2% Increase in of BSs (%) Omni Antenna Smart Antenna FoM (%) 100 86.6 90.5 3.5 95 78.4 80.5 5.6 90 76.3 82.4 6.5 80 76.0 81.3 4.2 70 74.6 78.7 1.9 60 74.6 75.2 0.9 50 71.7 75.0 -2.0 Table 6 The improvement in the FoM at different BS densities when a smart antenna is used instead of an omnidirectional antenna. Figure 14 shows simulation results for the uniform network run with a traffic density of 300 Erlangs per square kilometre. Note how, unlike in Figure 13, the trend downwards in the FoM is very smooth. The smart antenna results in the figure are not simulated. It would not be valid to simulate the smart antenna in this environment because the distinction between LOS and NLOS paths had no meaning with this model and we would not be observing the complete smart antenna model. Instead, we have shifted the blue omnidirectional curve up by 1.9% and 6.5% to generate curves for the minimum and maximum improvement in FoM due to the smart antennas. These two curves are shown as dashed and solid pink lines, respectively. We have limited the maximum FoM to 100%. Using the solid pink curve in Figure 14, at the FoM level of 95% the relative network density is reduced from about 65% to 55% when the maximum smart antenna is used instead of the omnidirectional antenna. This is equivalent to about a 15% reduction in the number of BSs. The lower bound improvement in BS density is only about 8% since the relative density decreases to about 60%. The reader should recall at this point that these numbers have been estimated using an example network and the precise improvement will vary depending on how well the network has been optimised for the conventional mobiles and the mobiles with adaptive antennas.
  • 59. 59/83 omni smart (FoM increased by 1.9%) smart (FoM increased by 6.5%) 100% 95% 90% FoM 85% 80% 75% 70% 40% 50% 60% 70% 80% 90% 100% Relative Number of Base Stations Figure 14 The FoM when the smart antenna is used in the uniform hexagonal network. The dashed and solid pink lines represent the minimum and maximum improvement in FoM due to the smart antenna being used in the handset. The traffic density is fixed at 300 Er/km2. 5.3 Increase in Date Rate Using the main network we ran simulations for various data rates. Figure 15 shows the effect of the data rate on the FoM when the offered traffic is 100 Erlangs per square kilometre. A low offered traffic level was chosen so that the FoM is not too low at high data rates. We can see that the smart antenna allows higher data rates. For example, when the FoM is 75% there is an improvement in the data rate from about 75 kbps to 115 kbps when changing from the omnidirectional antenna to the smart antenna. This is approximately a 50% increase in data rate. Although we also ran simulations at higher data rates, it was found that the results were inconclusive. This is because of the way that MACcdma’s simulation results are gathered. At the end of every snapshot an attempt is made to add a test mobile to ‘sample’ the coverage at each point in the network. It is assumed that this test mobile does not affect the network in any way. When the data rate is low there are many mobiles in the network and the assumption is valid, ie, one extra mobile makes little difference to the performance of the overall network. However, at higher data rates our assumption breaks down because there are not many mobiles in each cell. The test mobile in a real situation would significantly affect the performance of the overall network.
  • 60. 60/83 100% 90% 80% FoM omni 70% smart 60% 50% 40% 0 20 40 60 80 100 120 140 160 Data Rate (kbps) Figure 15 The effect of the data rate on the FoM when the offered traffic is 100 Erlangs per square kilometre. We can re-plot the data given in Figure 15 in a similar manner to that in Figure 12, giving Figure 16. This shows that there is a general trend downwards in the improvement in data rate due to the smart antenna as the data rate of the omnidirectional antenna increases, for a fixed FoM level.
  • 61. 61/83 120% 100% Gain in Data Rate 80% 60% 40% 20% 0% 0 50 100 150 Omni Data Rate (kbps) Figure 16 The gain in data rate achieved when a smart antenna is used instead of an omnidirectional antenna. The black line is a linear regression of the data. 5.4 Reduction in Dead Zone Size In Section 4.3.4 we explained why it was necessary to assume that dead zones exist within a 50 metre radius of each adjacent site. We simulated the main network using an offered traffic density of 100 Erlangs per square kilometre. One can see in the first two rows of Table 7 that the composite blocking probability within the 50 metre circles is virtually zero when no ACI is included. The adjacent network was then simulated using an offered traffic density of 100 Erlangs per square kilometre and the main network simulations were repeated, this time with the ACI included. The next two rows in Table 7 show the relevant results. In this case the composite blocking probability drops from 12.00 to 3.57 when the smart antenna is used. The smart antenna reduces the dead zone to less than a third of the original size using the omnidirectional antenna.
  • 62. 62/83 Radius Composite Ratio Include Traffic around Blocking (omni/smart) Antenna ACI Map (Er/km2) Adjacent Probability Sites in Circle Omnidirectional No 100 50 0.01 - Smart No 100 50 0.00 Omnidirectional Yes 100 50 12.00 3.4 Smart Yes 100 50 3.57 Table 7 Simulation parameters used to calculate the effect of the smart antenna on ACI. Note that we do not consider co- located sites. Offered traffic is 100 Er/km2. 5.5 Rural Link Budget The simulations we have run are for a UTRA FDD network in a dense urban environment. In this environment the network tends to be capacity limited. By this we mean that the offered traffic reaches the maximum available traffic. In a rural environment the offered traffic is significantly lower because the area is sparsely populated. In this environment the network tends to be coverage limited. This means that MSs can be so far from their serving BS that the signal strength received at the MS and/or the BS is at the noise floor and cannot be detected. There is a minimum required number of BSs to maintain coverage over a reasonable area of the network, even if these BSs are not fully loaded. To investigate the effect of using the smart antenna in a rural environment we have performed a simple link budget analysis. The same UTRA FDD parameters were used as those used in the simulations. We also assumed that there was no adjacent channel interference. Table 8 shows the UL budget that we have used. As one can see in the bottom row the maximum radius of the cell if it is 50% loaded is about 13.3 kilometres. This assumes that the COST231 Hata model has been used. The DL budget is given in Table 9. In this case we add another margin to account for the performance of the smart antenna. We have used the worst case improvement in antenna gain of 6.4 dB given in Section 3.1.3. This means that the improvement in the antenna gain is at least this value for 99% of the time. Notice that the DL maximum radii are 19.6 km and 29.8 km for the omnidirectional and the smart antenna, respectively. Since both of these distances are greater than the UL radius we conclude that the coverage in the rural environment is UL limited and smart antennas are unlikely to be useful without adaptive antennas being deployed at the BSs as well as in the mobiles. However, if
  • 63. 63/83 the traffic was heavily DL biased, ie, had higher data rates on the DL, the processing gain would be smaller. This in turn would mean that the DL radius would shrink and the use of smart antennas at the handset receiver would prove useful to improve the link. MS max transmit power 21 dBm a MS omni antenna gain 0 dBi b Effective isotropic radiated power 21 dBm c=a+b Thermal noise density -174 dBm/Hz d UL noise figure 5 dB e Receiver noise density -169 dBm/Hz f=d+e Chip rate 3840 kbps g Receiver noise power -103.1 dBm h=f+10log(g) RAKE efficiency factor 0.5 - i UL processing gain 315 - j UL required Eb/I0 5 dB k Receiver sensitivity -120.1 dBm l=h−10log(i)−10log(j)+k BS antenna gain 18.5 dBi m UL line loss 3 dB n Body loss 3 dB o UL loading margin 3 dB p Max path loss 150.6 dB q=c−l+m−n−o−p Log normal fading margin 7.3 dB r Allowed propagation loss 143.3 dB s=q−r MS height 2 m BS height 30 m Frequency 2 GHz Cell radius (COST231 Hata) 13.3 km Table 8 Link budget for the UL in a rural environment.
  • 64. 64/83 Omni Smart Antenna Antenna Max traffic channel power 32 32 dBm a BS antenna gain 18.5 18.5 dBi b Effective isotropic radiated power 50.5 50.5 dBm c=a+b Thermal noise density -174 -174 dBm/Hz d DL noise figure 9 9 dB e Receiver noise density -165 -165 dBm/Hz f=d+e Chip rate 3840 3840 kbps g Receiver noise power -99.1 -99.1 dBm h=f+10log(g) RAKE efficiency factor 0.5 0.5 - i DL processing gain 315 315 - j DL required Eb/I0 5 5 dB k Receiver sensitivity -116.1 -116.1 dBm l=h−10log(i) −10log(j)+k Mobile antenna gain 0 6.4 dBi m DL line loss 4 4 dB n Body loss 3 3 dB o DL loading margin 3 3 dB p Max path loss 156.6 163.0 dB q=c−l+m−n−o−p Log normal fading margin 7.3 7.3 dB r Allowed propagation loss 149.3 155.7 dB s=q−r MS height 2 2 m BS height 30 30 m Frequency 2 2 GHz Cell radius (COST231 Hata) 19.6 29.8 km Table 9 Link budget for the DL in a rural environment with and without the smart antenna. 6 Summary of Results and Conclusions Over the course of this project we have identified various features of the current state of the art of adaptive antenna technology for handsets. Many research journals were reviewed, experts in the field were consulted and companies at the leading edge of adaptive antenna technology were met. All of this was with the aim of understanding where the technology is
  • 65. 65/83 now and where we think it may be heading. As with any technology forecasting there is always an element of the unknown. However, based on our literature review we have extracted some key results that we might expect to see in commercial 3G systems in the coming few years. Essentially three technologies are currently feasible. MIMO antenna systems, although a promising technology, do not appear to be applicable in the near future in commercial 3G systems. Interference rejection has been seen to be useful but, due to the small number of antenna elements that can fit on a small handset, is limited to rejecting only a few interferers. In the presence of many strong interferers the simpler, maximal ratio combining diversity looks promising. We currently suggest that the handsets could employ a combination of both IRC and MRC. However, since the CDMA cellular environment is an interference-rich environment, and hence reduces the effectiveness of IRC, it may be interesting to compare the performance of IRC and MRC within a real 3G network. This we leave as a suggestion for further work. Many UTRA FDD simulations were run to find useful results. One key result, as one would expect, is that a smart antenna will allow more traffic onto the network. UTRA FDD is limited by the interference in the system and the smart antenna reduces the effect of this interference. In our network we found that about a third more traffic could use the network when the smart antenna was used instead of the omnidirectional antenna. However, although we have tried to use a network that approximately represents a real network, the specific improvement in capacity due to the smart antenna is likely to be network dependent. We also saw that the higher the traffic is, the less effect the smart antenna has. This is perhaps due to the fact that there will be a higher background noise level in a fully loaded network and the smart antenna is less able to overcome it. We next investigated what would happen to the FoM if the network density was reduced. As expected, it was found that the FoM tends to decrease as the network density was reduced. However, the FoM is also highly dependent on how well the network had been optimised. For example, removing one site may create a coverage hole that causes the FoM to drop suddenly in that area. We found that the smart antenna increased the overall network FoM by between about 1.9% and 6.5% in our example London network. To iron out the inconsistencies in network performance we also used a uniform hexagonal network. This was systematically scaled down in size and a performance improvement was found when using
  • 66. 66/83 the smart antenna. Approximately 8% to 15% fewer BSs are needed to achieve a FoM of 95% when the smart antenna is used instead of the omnidirectional antenna. The smart antenna was also found to allow higher data rates on the network. Between 20% to 100% higher data rates could be used with the smart antenna. The improvement was found to be highest for low data rates and to decrease when high data rates are used. The RA was also interested in identifying the reduction in the size of the dead zones that the smart antenna might facilitate. By assuming that dead zones only occur within 50 metres of an adjacent cell we found that the smart antenna could reduce the size of a dead zone to a third of that seen when an omnidirectional antenna is used. However, the reader should be aware that distinguishing between areas of high blocking caused by ACI and areas of high blocking due to co-channel interference is, to some extent, a subjective process. Finally, we considered the effects of using smart antennas in a rural environment. Simple link budgets were created for the UL and DL and the maximum distance a handset could be from a 50% loaded cell site was estimated. We found that the UL radius was about 13.3 kilometres compared to 19.6 kilometres for the DL when omnidirectional antennas were used on the handsets. When a smart antenna is used in the handset, the DL radius increases to about 29.8 kilometres. Therefore, in a rural environment the UL is the limiting link and any improvement to the DL, ie, through the use of adaptive antennas, would be of little benefit. However, if the traffic was heavily DL biased, the DL radius would shrink and the use of smart antennas at the handset receiver would prove useful to improve the link. Further, in this study we have assumed that no adaptive antenna technology has been used at the BSs. In practice it is more likely that adaptive antenna techniques could be used at the BS than in the handset because there is more power available to the BS. In addition, the BS can support more processing power for cleverer algorithms, since there is less of a restriction on size. Thirdly, antennas at BSs potentially can be separated enough to achieve good diversity gains. It is recommended that a similar study be carried out to consider the potential benefits of deploying advanced adaptive antenna technology at the BSs. Not only could this improve the UL, but it could also facilitate the implementation of more advanced techniques, such as MIMO technology. In light of the work that has been performed over the course of this project we recommend that the following work be carried out in the future. First, it is suggested that more
  • 67. 67/83 simulations be run. The time constraints of this project limited the number of simulations that could be performed. These were intended to establish general trends and performance gains of using smart antennas in handsets. More simulations will not only bring more confidence in the current results but also allow the trends to be analysed in more detail. Secondly, it would be a useful exercise to investigate the whole issue of radio resource management within the network. This relates to the decisions that the network makes as to whether or not a new user should be allowed onto the network or be blocked. This, in turn affects the dropping rate of other users that are already making a call. Life gets more complicated when there is a mixture of subscribers who have different service requirements and cost plans. The introduction of smart antennas further complicates the issue since some users will have very good coverage whilst those using omnidirectional antennas will be poorly serviced. Finally, as already discussed, we recommend that studies are performed of scenarios in which BS and MS adaptive antennas are combined into one “adaptive” system.
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  • 70. 70/83 28. Mostafa, R, Dietze, K, Palat, R C, Stutzman, W L and Reed, J H, “Demonstration of real-time wideband transmit diversity at the handset in an indoor wireless channel,” IEEE 54th Vehicular Technology Conference, Atlantic City, NJ, USA, Vol. 4, pp. 2072-2076, 7-11 October 2001. 29. ETSI/3GPP, Technical Specification Group Radio Access Network, Physical layer procedures (FDD), TS 25.214, Version 4.1.0, June 2001. 30. Kim, S W, Ha, D S, Kim, J H and Kim, J H, “Performance of smart antennas with adaptive combining at handsets for the 3GPP WCDMA system,” IEEE 54th Vehicular Technology Conference, Atlantic City, NJ, USA, Vol. 4, pp. 2048-2052, 7-11 October 2001. 31. Kim, S W, Ha, D S, Kim, J H and Kim, J H, “Performance gain of smart antennas with hybrid combining at handsets for the 3GPP WCDMA system,” Virginia Tech VLSI for Telecommunications Laboratory, downloaded from http://www.ee.vt.edu/~swkim/pdf/wpmc01_swkim.pdf, 22 October 2002. 32. Leach, S M, Agius, A A and Saunders, S R, “Intelligent quadrifilar helix antenna,” IEE Proceedings Microwaves, Antennas and Propagation, Vol. 147, No.3, pp. 219-223, June 2000. 33. Leach, S M, Agius, A A, Stavrou, S and Saunders, S R, “Diversity performance of the intelligent quadrifilar helix antenna in mobile satellite systems,” IEE Proceedings Microwaves, Antennas and Propagation, Vol. 147, No. 4, pp. 305-310, August 2000. 34. Dolmans, G and Leyton, L, “Performance study of an adaptive dual antenna handset for indoor communications,” IEE Proceedings Microwaves, Antennas and Propagation, Vol. 146, No. 2, pp. 138-144, April 1999. 35. Antenova, “New Generation Technology,” downloaded from http://antenova.com/tech.html, 23 October 2002. 36. Saunders, S R, Antennas and Propagation for Wireless Communication Systems, John Wiley & Sons, Chichester, 1999. 37. Pedersen, G F, Nielsen, J O, Olesen, K and Kovacs, I Z, “Measured variation in performance of handheld antennas for a large number of test persons,” IEEE 48th Vehicular Technology Conference, Ottawa, Canada, Vol 1, pp. 505-509, 18-21 May 1998. 38. Arai, H, Igi, N and Hanaoka, H, “Antenna-gain measurements of handheld terminals at 900 MHz,” IEEE Trans Vehicular Technology, Vol. 46, No.3, pp. 537-543, August 1997. 39. Scanlon, W G and Evans, N E, “Numerical analysis of body worn UFH antenna systems,” IEE Electronics & Communication Engineering Journal, pp. 53-64, April 2001.
  • 71. 71/83 40. “Wireless demand ups the ante for antennas,” Microwave Engineering, pp. 15-19, November 2002. 41. Morishita, H, Kim, Y and Fujimoto, K, “Design concept of antennas for small mobile terminals and the future perspectives,” IEEE Antennas and Propagation Magazine, Vol. 44, No. 5, pp. 30-43, October 2002. 42. Leisten, O P and Rosenberger, B, “Miniature dielectric loaded antennas with low SAR,” 10th International Conference on Wireless Communications, Calgary, Alberta, Canada, Vol. 1, pp. 196-205, July 1998. 43. Blake, L V, Antennas, John Wiley and Sons, New York, 1966. 44. Kreyszig, E, Advanced Engineering Mathematics, 7th ed., John Wiley and Sons, New York, pp. 1197-1199, 1993. 45. Braun, C, Engblom, G and Beckman, C, “Evaluation of Antenna Diversity Performance for Mobile Handsets Using 3-D Measurement Data,” IEEE Trans Antennas and Propagation, Vol. 47, No. 11, pp. 1736-1738, November 1999. 46. Holma, H and Antti, T, WCDMA for UMTS, Radio Access for Third Generation Mobile Communications, John Wiley and Sons Limited, Chichester, 2000. 47. ETSI/3GPP, Technical Specification Group Radio Access Networks, UTRA (BS) FDD, Radio transmission and Reception, TS 25.104, Version 5.0.0, September 2001.
  • 72. 72/83 Appendix A MACcdma Parameters MACcdma’s input parameters are separated into different pages within MACcdma’s Configuration Editor. They are presented here in a similar layout. 1 Simulation This group of parameters controls the operation of the simulation, ie, they do not relate directly to the network that is simulated. 1.1 Number of Simulation Snapshots The number of static snapshots that will be used to build up a picture of network performance. A higher number increases the statistical validity of the result, but also increases run time. A typical value of 100 was used; although 200 and 300 snapshots were also used at times to satisfy ourselves that 100 snapshots were statistically valid. 1.2 Global Traffic Scale Factor A scale factor applied to all traffic values in the simulation. This was varied depending on how much traffic we wanted to simulate. 1.3 Output Bin Size The resolution of the output plots. We followed the same resolution as the coverage predictions, ie, 5 m. 1.4 Output Statistics Area A 1 km × 1 km area at the centre of our 5 km × 5 km map was used. 2 Network This group of parameters relates to the network as a whole, ie, parameters affect every base station or mobile, as appropriate. 2.1 Orthogonality Factor The proportion of the orthogonally transmitted power that will be seen as interference at the receiver. A typical value of 0.4 was used.
  • 73. 73/83 2.2 Pilot Channel Required Ec/I0 The required ratio of energy per chip to interference power spectral density. A typical value of -16 dB was used. 2.3 Attenuation of Extra Interference Attenuation applied to the additional DL interference, which is supplied via an additional interference map file. The 3GPP standard value of 33 dB for a 5 MHz carrier offset was used1. 2.4 RAKE Efficiency Factor The proportion of the received signal that is actually captured by the RAKE receiver. This value is set to 0.5. 2.5 Down Link Noise Figure The noise figure of the mobile receiver. A typical value of 9 dB2 was used. 2.6 Down Link Line Loss Additional loss that should be added to all DL connections to model cable and connector losses. A typical value of 4 dB was used. 2.7 Up Link Noise Figure The noise figure of the base station receiver. A typical value of 5 dB3 was used. 2.8 Up Link Line Loss Additional loss that should be added to all UL connections to model cable and connector losses. A typical value of 3 dB was used. 1 ETSI/3GPP, Technical Specification Group Radio Access Networks, UE Radio Transmission and Reception (FDD), TS 25.101, Version 5.0.0, September 2001. 2 Laiho, J, Wacker, A and Novosad, T, Radio Network Planning and Optimisation for UMTS, John Wiley and Sons Limited, Chichester, 2002. 3 Holma, H and Antti, T, WCDMA for UMTS, Radio Access for Third Generation Mobile Communications, John Wiley and Sons Limited, Chichester, 2000.
  • 74. 74/83 2.9 Maximum Traffic Channel Power The maximum DL power that can be allocated to a single user. We used a typical value that is 1 dB below the pilot channel power. If we assume the maximum total DL power is 43 dBm and the relative pilot channel power, as described in Section 2.11, is 0.1, the maximum traffic channel power is 32 dBm. 2.10 Minimum Traffic Channel Power The minimum DL power that can be allocated to a single user. The value was set to -40 dBm. 2.11 Relative Pilot Channel Power The proportion of maximum total DL power that is allocated to the pilot channel. A typical value of 0.1 was used. 2.12 Relative Common Channels Power The proportion of maximum total DL power that is allocated to the common control channels (excluding the pilot channel). If, from Section 2.11, 10% of the DL power is dedicated to the pilot channel and, from Section 2.13, 76.8% of the DL power is in the traffic channel, we are left with the relative common channels power at 0.132. 2.13 Relative Total Traffic Power The proportion of maximum total DL power that is available for allocation to traffic channels. A typical value of 0.768 was used. 3 Services The service parameters define the 3G service types active in the network. A set of output plots is produced by the simulation for each defined service type. We defined several different service types, and they were simulated individually rather than using a service mix. The two principal service types are voice circuit data and packet data and Table A.1 shows the parameters that were used with these service types. Table A.2 shows those parameters that are specific to each data rate. The definition of these parameters follows these two tables.
  • 75. 75/83 Voice Data Parameter Packet Data (12.2 kbps) Max MS Transmit Power (dBm) 21 24 SHO Enabled Yes Yes No. of Secondary UL Channels 1 1 UL Processing Gain 315 see Table A.2 UL Required Eb/I0 (dB) 5 see Table A.2 UL Channels of this Type 1 (1) 1 (1) UL Source Activity Factor 0.5 (1.0) 0.8 (1.0) UL Transmit Cycle 1.0 (1.0) 0.025 (1.0) UL Relative Power of Secondary Channel (dB) -2.69 -9.54 No. of Secondary DL Channels 0 0 DL Processing Gain 315 see Table A.2 DL Required Eb/I0 (dB) 5 see Table A.2 DL Channels Of This Type 1 1 DL Source Activity Factor 0.5 0.8 DL Transmit Cycle 1 0.975 Table A.1 Service specific parameters used in the simulations. Numbers in brackets refer to secondary channel values. Bit Rate (kbps) UL/DL Processing Gain UL/DL Required Eb/I0 (dB) 44 87 2.5 64 60 2.0 104 37 1.5 144 27 1.5 204 19 1.5 264 15 1.0 324 12 1.0 384 10 1.0 Table A.2 Data rate specific parameters used in the simulations. 3.1 Max MS Transmit Power The maximum transmit power capability of mobiles using this service.
  • 76. 76/83 3.2 SHO Enabled Enables mobiles of this service to use soft handover (SHO). 3.3 Processing Gain The processing gain, given by the chip rate (3.84 Mchip/s for UTRA FDD) divided by the bit rate of this service. 3.4 Required Eb/I0 The required ratio of energy per traffic channel bit to the interference power spectral density. 3.5 Channels of this Type The number of physical channels of this type. For example, a UTRA call can have up to six traffic channels. 3.6 Source Activity Factor The proportion of time for which a transmitting source is active when nominally engaged in transmissions. 3.7 Transmit Cycle The proportion of time for which a service is nominally engaged in transmissions. 3.8 Relative Power The transmit power of a secondary channel relative to the instantaneous power of the associated primary channel. 4 Call Admission Control Some parameters relate to the CAC algorithm used in the network. The purpose of a CAC algorithm is to prevent new users accessing the network if their transmission would adversely affect the quality of ongoing calls. The CAC assessment can be based either on the current network status, or an attempt can be made to predict the impact of the new call. CAC algorithms can operate on the DL, UL or both.
  • 77. 77/83 4.1 Call Admission Control Algorithm The “down link predictive transmit power, up link predictive SIR CAC” algorithm was used. New call attempts are accepted if a certain power headroom exists at the target base station, including the power that will be allocated to the new call, and if no existing mobiles attached to the target base station will have their UL Eb/I0 reduced beyond the specified value with the interference generated by the new call. 4.2 Down Link Power Headroom This value is the minimum margin allowed below the maximum traffic channel power before a new user is allowed onto the network and was set to 1 dB. 4.3 Maximum Reduction in Up Link This value is the maximum degradation allowed in the UL Eb/Io before a new user is not allowed onto the network and was set to 0.5 dB. 5 Soft Handover The final group of parameters relates to the SHO algorithm implemented in the network. 5.1 Up Link Margin The Eb/I0 target for each UL in the Active Set can be reduced by this amount. A typical value of 2 dB was used. 5.2 Maximum Active Set Size The maximum number of BSs in the Active Set. A typical value of three was used. 5.3 Add/Drop Threshold The difference between the Ec/I0 of the serving and candidate cells for the candidate cell to be added to/dropped from the Active Set. A typical value of 3 dB was used. 5.4 Add/Drop Hysteresis This value is subtracted from the Add/Drop Threshold when adding candidates to the Active Set, and added when removing candidates from the Active Set. A typical value of 0.5 dB was used.
  • 78. 78/83 5.5 Replacement Hysteresis When the Active Set is full, a further candidate will replace the weakest cell when its Ec/I0 exceeds that of the weakest cell by this amount. A typical value of 0.5 dB was used.
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