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Prediction of wireless communication systems in the context of modeling 2-3-4

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  • 1. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN INTERNATIONAL JOURNAL OF ELECTRONICS AND 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEMECOMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)ISSN 0976 – 6464(Print)ISSN 0976 – 6472(Online)Volume 4, Issue 1, January- February (2013), pp. 11-17 IJECET© IAEME: www.iaeme.com/ijecet.aspJournal Impact Factor (2012): 3.5930 (Calculated by GISI) ©IAEMEwww.jifactor.com PREDICTION OF WIRELESS COMMUNICATION SYSTEMS IN THE CONTEXT OF MODELING T.Regua, Dr.G.Kalivarathanb a Research Scholar, CMJ University, Meghalaya, Shillong. b Principal/ PSN Institute of Technology and Science, Tirunelveli, Tamilnadu, Supervisor, CMJ University, Shillong. Email:sakthi_eswar@yahoo.com ABSTRACT This paper is focused with the use of numerous antenna elements in wireless communication over frequency non-selective radio channels. Both experimental results and theoretical analysis are discussed with definite details. New transmit strategies are derived and balanced to existing transmit strategies, such as beamforming and space time block coding (STBC). It is seen that the best transmission algorithm is principally dependent on the channel characteristics, such as the number of transmit and receive antennas and the continuation of a line of sight component. Rayleigh desertion multiple input multiple output (MIMO) channels are studied using an eigenvalue analysis and faithful expressions for the bit error charge and outage capacities for beamforming and STBC is found. In general MIMO fading channels are correlated and there exists a mutual coupling between antenna elements. These investigations are supported by indoor MIMO measurements. It is seen that the mutual coupling can, in some scenarios, increase the outage capacity. An adaptive antenna testbed is used to obtain measurement results for the SIMO channel. The results are analyzed and design oriented guidelines are obtained for how a beamformer executed in hardware shall be constructed. The property of nonlinear transmit amplifiers in array antennas are also analyzed, and it is seen that an array condenses the effective intermodulation distortion (IMD) transmitted by the array antenna by a spatial filtering of the IMD. A novel frequency allocation algorithm is proposed that reduces IMD even additional. The use of a low cost antenna with switchable directional properties, the switched freeloading antenna, is studied in a MIMO context and compared to array techniques. It is found that it has comparable performance, at a fraction of the cost for an array antenna. Keywords: antenna array, calibration, mutual coupling, MIMO system, flat fading, nonlinear amplifier, switched parasitic antenna, analog beamformer, adaptive antenna testbed. 11
  • 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEME1.0 INTRODUCTION Wireless systems are now popular worldwide to help people and machines to communicatewith each other irrespectively of their location. So far, using a cellular system is by far the mostcommon wireless method to access data or to perform voice dialing. But in a near future, we will besurrounded by a numerous of options to set up an unwired connection over the radio interface. One ofthe slogans for the fourth generation wireless communications system (4G) is “always bestconnected”, meaning that your wireless equipment should connect to the network or system that at themoment is the “best” for you. Various connections ranges from satellites that provides low bit ratesbut global coverage and cellular systems with continental coverage to high bit rate local area networksand personal area networks with a maximum range of a few to a hundred meters. If these systemsshould co-exist, then we would obtain a crowded frequency spectrum, since there are many differentactors that want their share of the limited frequency resource. To use a signaling strategy that isspectrally efficient is thus of utmost importance. The current trend to achieve high spectral efficiencyis by utilizing adaptivity in the ever changing radio environment and sources of interference.Adaptivity on the physical layer can be used in all possible dimensions: Time, frequency, power andspace. Adaptivity can also be used on higher signaling layers to boost performance even further; anexample is multi-user scheduling. This thesis is devoted to the physical layer of wirelesscommunication systems and will focus mainly on the adaptive utilization of the space dimension.Space utilization is possible through the use of multiple antenna elements arranged in an array, for thetransmission and/or reception of the signals. Or, in some cases a single antenna element that hasseveral polarizations or modes is used to obtain polarization or angle diversity. Due to the use ofmultiple antennas, the antenna gain is increased and this leads to an increased range and coverage.This is useful in remote areas with low population. A large area can thus be served with less basestations. Alternatively, the transmit power of the mobile units can be reduced due to the increasedgain, or sensitivity, of the receiving base station antenna array. By using the spatial dimensionprovided by multiple antenna elements, it is possible to suppress interfering signals in a way that isnot possible with a single antenna. Hence, the system can be tuned to be less susceptible tointerference and the distance between base stations using the same time/frequency channel can bereduced, which is beneficial in densely populated areas. This leads to a system capacity improvement.A receiver array antenna can be used to localize the transmitter, just as we can use our both ears tolocalize the source of a sound in a room without using our eyes. This has application in positioningservices and emergency call localization. The maximum likelihood (ML) detector for linear space-time codes used over a flat fading MIMO channel with spatially and temporally colored Gaussiannoise is now derived. The derivation gives insight to the problems associated with spatial multiplexingtransmission and the resulting detector is used in other sections of this thesis. Furthermore, thepairwise error probability (PEP) for the linear space time code is also studied. The PEP characterizesthe performance of a system with coding over a finite number of blocks, M, and captures the diversityadvantage of a code.2.0 MIMO SYSTEM PERFORMANCE MIMO systems in flat fading channels are in this chapter analyzed and compared, with andwithout partial Channel State Information (CSI) at the transmitter, under the assumption of differentfading statistics. It will be assumed that the signals from different receive and/or transmit antennas arecorrelated. Mutual coupling is also introduced between the antenna elements. Measurement resultsfrom a MIMO testbed are presented and it is verified that the Rayleigh fading assumption is valid insome cases. Accurate and tractable channel modeling is critical to realize the full potential of antennaarrays. Two subgroups of channel models for MIMO systems can be identified. One is based on thephysical properties of the channel, and is a parametric model where the parameters are; the number ofscatterers, angle of arrival of the signal from the scattered, time delay, and power decay profiles.These parameters are often modelled as random variables from a given distribution. Such models can 12
  • 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEMEadapt to the multipath environment and the array geometry and its flexibility makes themattractive for computer simulations. For analytical derivations, however, a reductionisticapproach is taken, where random matrix theory is used to define the channel with fewerparameters than in the physical model, although at the expense of lower accuracy in capacityprediction. To generate signal correlation matrices CR and CT, a model that resembles thephysical scattering in the channel can be used. Many different approaches to model signalcorrelation, or directly the channel matrix for MIMO systems have been presented.3.0 STOCHASTIC CHANNEL MODELS The propagation scenario in a wireless communication system is very complex andthe signal transmitted from an antenna will reach the receiving antenna after many pathreflections. If the scattering is rich enough, then a stochastic method is suitable to model thechannel. The well known stochastic models for the SISO channel are in this section extendedto the MIMO channel. The elements of the H matrix are assumed to be random variablestaken from a probability distribution function (PDF). The elements are in general correlatedbut sometimes the correlation is neglected in an initial analysis to make it tractable. Thecorrelation depends on the scattering scenario, the antenna element radiation patterns, theirconfiguration and separation distance. It is modelled using a general correlation model, wherethe covariance matrix of the channel matrix elements is defined. However, in the definition ofthe Rayleigh fading MIMO channel, a special structure will be imposed on the correlationmatrix. This structure makes further analysis of the Rayleigh fading MIMO case possible,since the joint pdf of the eigenvalues to HH* for this case is known.4.0 SIGNAL CORRELATION An important property of the MIMO channel that essentially determines the channelcapacity is the correlation between the channel coefficients. When multi antenna systems areanalyzed, it is commonly assumed that the fading between pairs of transmit and receiveantennas are independent and identically distributed random variables with a Rayleighdistribution5.0 MEASURED MIMO CHANNELS To collect real MIMO channel data, measurements were performed in an indoorenvironment at the Signals and Systems Group, Uppsala University. The aim of themeasurements was to verify the assumptions made in the theoretical analysis regarding fadingdistribution, signal correlation and the flat fading assumption. The measurements wereperformed in an indoor office environment using a 4 × 4 MIMO system at the frequency 1.8GHz. A Vector Network Analyzer was used to measure the channel coefficients for the 16channels using a switching method.Each complete “MIMO snapshot” of all the 16 channels were measured in less than 3seconds, which is fast enough in indoor environments to ensure that the channel remainsstationary during each measurement. The switching method was also used on an 8×8 MIMOsystem. Between each measurement, the receiving array was moved one eight of awavelength in the broadside direction. The antenna elements were microstrip patch antennasplaced in a linear array with an inter-element spacing (δ) of half a wavelength (δ = λc/2). Thepatch antennas had a half power beam width of 80◦ and a half power bandwidth of 170 MHz 13
  • 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEMEand the measurement SNR was set to 20 dB in all measurements. Two scenarios wereinvestigated, one line of sight (LOS) and one non-LOS (NLOS) setup. In the LOS scenario,the two arrays were placed facing each other in an 8×6 meter laboratory room containingvarious instruments, tables and cabinets and ND = 146 measurements were conducted. In theNLOS scenario, ND = 220 measurements were conducted and the receive array was placedoutside the laboratory room, centered in a long corridor with the array broadside parallel withthe corridor. The transmit array was kept in the adjacent laboratory. To make comparisonswith the theoretical models, each element of the measured H matrix was normalized as6.0 COHERENCE BANDWIDTH The initial measurements aimed to verify the flat Rayleigh fading assumption. Themeasured power spectrum in the NLOS case from one transmit antenna to the four receiveantennas. The coherence bandwidth (at correlation coefficient 0.9) is estimated to Bc = 2.8MHz so the flat fading assumption is valid if the signalling bandwidth is less than Bc. If asystem with higher bitrate is required, then transmission over many subchannels can be used,where the bandwidth of each subchannel is less than BcFig.1. Power spectrum for NLOS channel. Each curve represents the received power in one out of four half-a-wavelength spaced antennas from one transmit antenna. 14
  • 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEMEFigure 2 shows the normalized channel amplitudes for all 16 channels in the NLOS case andit is immediately apparent that the channels fading patterns are different. The correlationbetween these subchannels is further explored below. In Figure 3, the estimated probabilitydensity functions of the normalized amplitudes in the LOS and NLOS cases are shown. Thecurves are fitted to a Nakagami-m distribution using a moment based method [100]. TheNakagami-m distribution .The mf -parameter was estimated to mf = 1.17 in the NLOS caseand mf = 6.41 in the LOS case. The measured data was also used FIG.2. Channel amplitudes in a 4 × 4 MIMO NLOS channel as a function of measurement.An important property of the MIMO channel that essentially determines the channel capacityis the correlation between the channel coefficients. When multi antenna systems are analyzed,it is commonly assumed that the fading between pairs of transmit and receive antennas areindependent and identically distributed random variables with a Rayleigh distribution. On theother hand, a small antenna element spacing is often necessary to fit multiple antennas on aportable device. Small element spacing also introduces mutual coupling between the antennaelements which affects the achievable capacity of the system, although not necessarily in anegative way the distances from the different transmitting array antennas via the scattered tothe receiver array are approximately equal, so the correlation between two paths fromdifferent transmit elements to one receive elements is expected to be high as compared to thelarge 15
  • 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEME FIG.3. CUMULATIVE DISTRIBUTION OF PHASE IN NLOS CHANNEL.7.0 CONCLUSIONSThe aim of this work was to investigate the impact of using MCPA’s at the base stationfor the downlink in a wireless system. The derivations was simplified by assuming a switchedbeam configuration, often implemented using a beamforming network, such as the Butlermatrix. This technique was shown to have the interesting property that the IMD follows thesame radiation patterns as the original desired signals patterns. This allowed for the conceptof a beam-frequency scheme and a frequency channel allocation algorithm for reducing theharmful IMD in that particular cell was developed. A base station with more antennaelements gave a reduction of intermodulation distortion which could be of the order ofseveral dB.REFERENCES[1] P.K. Bondyopadhyay, “The first application of array antenna,” in Proceedings of IEEEInternational Conference on Phased Array Systems and Technology., Dana Point , USA,2000, pp. 29–32.[2] S. Andersson, B. Carlqvist, B. Hagerman, and R. Lagerholm, “Enhancing cellularnetwork capacity with adaptive antennas,” Ericsson Review, vol. 76, pp. 138–141, 1999.[3] H. Dam, M. Berg, S. Andersson, R. Bormann, M. Frerich, and T. Henβ, “Performanceevaluation of adaptive antenna base stations in a commercial GSM network,” in Proceedingsof Vehicular Technology Conference (VTC), Piscataway, USA, 1999, pp. 47–51. 16
  • 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEME[4] K. Sheikh, D. Gesbert, D. Gore, and A.J. Paulraj, “Smart antennas for broadband wirelessaccess networks,” IEEE Signal Processing Magazine, vol. 37, no. 11, pp. 100–105, Nov.1999.[5] R.T. Derryberry, S.D. Gray, D.M. Ionescu, G.Mandyam, and B. Raghothaman, “Transmitdiversity in 3G CDMA systems,” IEEE Communications Magazine, vol. 40, no. 4, pp. 68–75,April 2002.[6] S. Andersson and B. Hagerman, “Adaptive antennas in wireless systems basicbackground and field-trial results,” in Proceedings of Radiovetenskaplig Konferens,Karlskrona, Sweden, 1999, pp. 249–253.[7] S. Andersson, U.Forss´en, J.Karlsson, T.Witzschel, P. Fischer, and A. Krug,“Ericsson/Mannesmann GSM field trials with adaptive antennas,” in Proceedingsof Vehicular Technology Conference (VTC), Phoenix,USA, May 1997, pp. 1587–1591.[8] J. Strandell, M. Wennstro¨m, A. Rydberg, T. O¨ berg, O. Gladh, L. Rexberg, E. Sandberg,B.V. Andersson, and M. Appelgren, “Experimental evaluation of an adaptive antenna for aTDMA telephony system,” in IEEE Personal Indoor and Mobile Radio CommunicationsConference (PIMRC), Helsinki, Finland, Sep. 1997, pp. 79–84.[9] J. Strandell, M. Wennstro¨m, T. O¨ berg, and A. Rydberg, “Design and evaluation of afully adaptive antenna for telecommunication systems,” in Proceedings Antenn97 conference,Gothenburg, Sweden, 1997, pp. 357–366.[10] S.M. Simmonds and M. Beach, “Downlink calibration requirements for theTSUNAMI(II) adaptive antenna testbed,” in Proceedings of the Ninth InternationalSymposium on Personal, Indoor and Mobile Radio Communications, Boston, USA, 8-11September 1998.[11] P.E. Mogensen, K.I. Pedersen, P. Leth-Espensen, B. Fleury, F. Fredriksen, andK.Olesen, “Preliminary results from an adaptive antenna array testbed for GSM/UMTS,” inProceedings of Vehicular Technology Conference (VTC), Phoenix,USA, May 1997, pp.1592–1596.[12] G. Tsoulos, M. Beach, and J. McGeehan, “Space division multiple acess (SDMA) fieldtrials. Part 2: Calibration and linearity issues,” IEE Proceedings - Radar, Sonar andNavigation, vol. 145, no. 1, pp. 79–84, Feb. 1998.[13] S.M. Alamouti, “A simple transmit diversity technique for wireless communications,”IEEE Journal on selected areas in communications, vol. 16, no. 8, pp. 1451–1458, Oct. 1998.[14] T. O¨ berg, Modulation, Detection and Coding. Chichester: John Wiley and Sons, 2001.[15] J.G. Proakis, Digital Communications. Singapore: McGraw-Hill, 1989.[16] Dr. V. Murali Krishna, Karimella Vikram and Prof. Narasimha, “Broadband WirelessCommunication” International journal of Electronics and Communication Engineering&Technology (IJECET), Volume3, Issue2, 2012, pp. 217 - 226, Published by IAEME.[17] Gangadhar P Maddani, Sameena N Mahagavin and Shivasharanappa N Mulgi,“Rectangular Microstrip Array Antennas For Wide Triple Band Operation” Internationaljournal of Electronics and Communication Engineering &Technology (IJECET), Volume1,Issue1, 2010, pp. 53 - 61, Published by IAEME.[18] Gangadhar P Maddani, Sameena N Mahagavin and Shivasharanappa N Mulgi, “DesignAnd Development Of Microstrip Array Antenna For Wide Dual Band Operation”International journal of Electronics and Communication Engineering &Technology(IJECET), Volume1, Issue1, 2010, pp. 107 - 116, Published by IAEME. 17