SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                                      1

SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                         2

SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                      3

interesting for the ...
SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                       4

capacity of spatial...
SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                       5

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SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                        6

are to contribute ...
SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                      7

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SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                      8

consumption of the m...
SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                       9

of channel state in...
SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                   10

processing algorithms ...
SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                       11

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SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                     12

systems with univers...
SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                                               ...
SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                                               ...
SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                                              1...
SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                                               ...
SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                                               ...
SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                                               ...
SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE                                                                               ...
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MIMO in 3G Cellular Systems: Challenges and Future Directions


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MIMO in 3G Cellular Systems: Challenges and Future Directions

  1. 1. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 1 MIMO in 3G Cellular Systems: Challenges and Future Directions Jeffrey G. Andrews, Wan Choi, and Robert W. Heath Jr. Abstract For 3G cellular systems to compete in the mobile data market with emerging technologies like WiMax/802.16 in the medium to long-term, multi-antenna transmission and reception (known as MIMO) will be required to achieve the requisite high data rates. One of the greatest challenges facing MIMO in the context of 3G is that present MIMO systems do not cope gracefully with high levels of interference. Since any well-designed cellular system is by nature interference-limited, this poses a fundamental conflict: increasing the spectral efficiency with MIMO appears to require reducing the interference level, which traditionally requires increased frequency reuse or other spectral efficiency reducing measures. In this paper, we overview and compare recent approaches for multicell MIMO and explain their shortcomings for 3G systems, or indeed any heavily-loaded cellular network. We then introduce two simple and complementary interference-reducing techniques that may prove effective and practical for adding MIMO to 3G cellular, and other future wireless broadband systems. The authors are with the Wireless Networking and Communications Group, Department of Electrical and Com- puter Engineering, The University of Texas at Austin, 1 University Station C0803, Austin, TX 78712, USA. Email:{jgandrews, wchoi, rheath} Phone:1-512-471-0536. This work was supported in part by SOLiD technologies, Korea, and Freescale Semiconductor. April 29, 2005 DRAFT
  2. 2. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 2 I. T HE P ROMISE OF MIMO The idea of using multiple receive and multiple transmit antennas has emerged as one of the most significant technical breakthroughs in modern wireless communications. Theoretical studies and initial prototyping of these multiple-input multiple-output (MIMO) systems have shown order of magnitude spectral efficiency improvements in point-to-point communication. As a result, MIMO is considered a key technology for improving the throughput of future wireless broadband data systems, which presently are mired at data rates far below their wired counterparts. The multidimensional MIMO channel can be exploited to increase the diversity of the system or to provide parallel spatial channels, which is known as spatial multiplexing. Diversity is generally considered lower risk, and a well-known example is space-time codes [1], [2], which have found adoption in 3G CDMA cellular systems [3]. Diversity increases the robustness of the system by eliminating fades, and also raises the average received signal to noise ratio (SNR). Since the SNR increases linearly with the diversity order, the capacity growth is logarithmic, easily verified with Shannon’s formula C = B log2 (1 + SN R). On the other hand, spatial multiplexing divides the incoming data into multiple parallel substreams and transmits each on a different antenna. If successfully decoded, it is logical that this increases the capacity linearly with the number of transmit antennas (or communication dimensions), which indeed has been proven using information theory [4]. Spatial multiplexing is thus more exciting than spatial diversity from a high-data rate point of view, but due to the fading in the wireless channel, some diversity is generally needed in a practical system to get an acceptable SNR and hence error probability. Therefore, a future high throughput system is likely to use some of the available dimensions for spatial multiplexing, and some for diversity. In this paper, we focus primarily on the spatial multiplexing aspects of MIMO as they are more exciting as far as increasing capacity and more challenging. Since high data rates are particularly April 29, 2005 DRAFT
  3. 3. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 3 interesting for the downlink, it can be assumed that the number of transmit antennas Mt will be larger than the number of receive antennas Mr , due to extreme space and cost restrictions on the mobile unit. It is reasonable to expect that Mr of the transmit antennas will be used for spatial multiplexing, while the remaining Mt − Mr can be used for transmit diversity, using antenna subset selection for example [5]. II. MIMO IN 3G C ELLULAR S YSTEMS Despite all the excitement surrounding MIMO’s promised capacity increases, it is quite a different matter to apply it successfully to a commercial cellular system. The vast majority of academic and even industrial research has focused on the point-to-point model (ignoring nearby competing interference sources), which is well-summarized in [6]. Nevertheless, MIMO’s enormous data rate incentives have motivated much interest from 3G cellular providers and equipment manufacturers, and MIMO is being widely considered for cdma2000 (3GPP2) and WCDMA (3GPP), particularly for the high-data rate modes such as EV-DV, EV-DO, and HSDPA [7]. For example, for 3GPP a combination of V-BLAST and spreading code reuse has been considered and the hypothetical peak data rates with this combination are given in Table I [8]. Table I is a typically naive interpretation of how MIMO can be applied to an interference- limited cellular system. All well-designed cellular systems are by nature interference-limited: if they were not, it would be possible to increase the spectral efficiency by lowering the frequency reuse or increasing the average loading per cell. In the downlink of a cellular system, where MIMO is expected to be the most profitable and viable, there will be an effective number of N Mt interfering signals, if the number of non-negligible neighboring base stations is N . Figure 1 illustrates the impact of other-cell interference in cellular MIMO systems. III. T HE OCI P ROBLEM IN C ELLULAR MIMO C OMMUNICATIONS There have been a few notable preliminary studies on interference-limited MIMO networks [9], [10] with the general conclusion that other-cell interference severely degrades the overall April 29, 2005 DRAFT
  4. 4. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 4 capacity of spatial multiplexing MIMO systems. MIMO receivers are able to decode the parallel data streams by suppressing the spatial interference between the signals sent from the Mt adjacent transmit antennas. The root cause of the degradation is that the MIMO receiver has to focus on suppressing the spatial interference introduced by the multiantenna transmitter; consequently, there are not sufficient degrees of freedom available to also suppress the co-channel interference, be it from other users in the same cell or from other cells (other-cell interference). Recently, a closed-form expression for interference-limited MIMO cellular system performance was developed, specifically for the outage probability and outage capacity of a MIMO-CDMA system with linear receivers [11]. Although one of the primary advantages of CDMA is its resilience to co-channel interference, this paper demonstrated that OCI can severely degrade the overall capacity of spatial multiplexing even in CDMA systems. Figure 2 shows this disappointing result, which assumes that the spatial interference is completely suppressed by a zero-forcing linear receiver in order to separate out of the different spatial streams. In particular, it can be seen that a conventional 1 × 1 cellular CDMA system in fact performs similarly to an 8 × 8 system with the same total rate (and hence 8 times the spreading factor). Although a suboptimal Minimum Mean Square Error (MMSE) receiver can reduce the noise enhancement by allowing some residual spatial interference, the overall improvement is incremental. While the problem of other-cell interference has existed in cellular systems for many years, its effect on MIMO systems is far more severe, and also more intriguing. Traditional OCI-reduction techniques have been quite limited, such as sectoring cells using simple directional antennas, or increasing the frequency reuse distance. In the rest of the paper, we discuss more sophisticated recent strategies that have been proposed for interference-limited multicell MIMO systems, and consider their promise for 3G wireless networks. April 29, 2005 DRAFT
  5. 5. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 5 IV. OCI M ITIGATION T ECHNIQUES FOR 3G C ELLULAR MIMO S YSTEMS In this section, we outline possible OCI mitigation techniques in 3G cellular MIMO systems and discuss their feasibility. A. Frequency reuse Traditionally, frequency reuse has been adopted for OCI reduction in cellular systems owing to its simplicity and practicality. Frequency reuse effectively reduces OCI by spacing the competing transmissions farther away, and particularly benefits users near the cell boundaries. This of course reduces the spectral efficiency, since now only 1/f of the available spectrum is used each cell, if f is the “frequency reuse factor”, which is generally between 3-7 in non-CDMA systems. One of the great capacity and deployment advantages of CDMA is its allowance of universal frequency reuse (i.e. f = 1), so simple static frequency reuse is not an attractive option for MIMO-CDMA in 3G cellular. This will be shown more concretely in the next section. B. Advanced Receiver Techniques As prior studies [10], [11] suggested, interference-aware MIMO receivers can significantly attenuate the OCI and hence greatly improve system performance. Note here that we are focused on suppressing other-cell interference, as opposed to self-cell interference. The latter is usually the emphasis in most multiuser receiver literature. However, cancelling the self-cell interference is not that interesting in 3G CDMA since orthogonal codes are used in the downlink, so a chip-level equalizer is the most effective means for reducing self-cell interference, since minus multipath, the self-cell users do not interfere with each other. Such chip-level equalizers are under wide investigation in industry, and a 3GPP study group has been initiated by Nokia for this very purpose. So instead, we overview advanced signal processing options available to the receiver to suppress other-cell interference, and explain the key challenges these techniques face if they April 29, 2005 DRAFT
  6. 6. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 6 are to contribute to the adoption of MIMO in 3G cellular systems. 1) (Near) Maximum Likelihood Multiuser Detection: If instantaneous information on the channels of interferers is available, maximal likelihood (ML) Multiuser Detection (MUD) is known to minimize the bit error probability in a multicellular MIMO system. However, not only is this instantaneous channel information difficult to attain for neighboring base stations, the complexity of such a receiver is prohibitive for a low-power mobile unit. For an Mr × Mr MIMO system using M-QAM , the complexity is on the order of M N Mr , where N is the number of interferers [10]. Even for simple cases this is well beyond reasonable implementation complexity. An alternative is the sphere decoder, which searches for the optimal solution in a sphere around an estimate of the received codeword. Sphere decoders can deal with moderate N Mr but still fail for large numbers of antennas or interferers. Additionally, they have variable complexity making implementation difficult. VLSI implementations are under development [12] but their viability in power hungry mobiles is still open to debate. 2) The MMSE Receiver: Due to the complexity problems associated with the ML receiver, a natural approach is to consider a linear approximation to the ML receiver. As shown in Fig. 2, the zero-forcing spatial receiver performs terribly due to noise-enhancement, which suggests the need for an MMSE receiver that balances noise-enhancement with spatial interference suppression. Here, two classes of MMSE receivers can be considered: one that knows the interferer’s channels (MMSE MUD) and one that does not know anything about the interference other than its autcorrelation (OCI-blind MMSE). Naturally, the MMSE MUD technique has superior performance, but suffers from two im- portant problems. First, as in the ML detector, a major difficulty is attaining instantaneous channel knowledge for the interfering base stations. Secondly, because Mt antennas at each of N interfering base stations must be treated as Mt independent interferers (for a total of Mt N interferers), effectively suppressing all of this interference is very difficult with only Mr Mt N receive antennas. One simplification, which is being looked at by Cingular and others, is to cancel April 29, 2005 DRAFT
  7. 7. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 7 just the pilot and common channel signals of the strongest interfering base station, which also alleviates the channel estimation requirements. However, the network-wide capacity gains from doing this are predicted to be around 10-25%, which is perhaps worthy of implementation but not a major leap forward. Due to these challenges, an OCI-blind MMSE receiver can be implemented with only a power estimate of the covariance of the total received signal Ryy instead of exact channel knowledge for all the interfering signals. Essentially, this simply introduces a noise scaling term in the spatial matrix inversion. But in this case, the performance is only incrementally better than the zero-forcing receiver. 3) Interference Cancelling Receivers: As observed in single antenna systems, nonlinear re- ceivers often provide a desirable tradeoff between performance and complexity [13], relative to ML receivers at one extreme and linear receivers at the other. In multicell MIMO systems, group detection techniques have a natural appeal, in which information bits for each “group” (cell) are detected sequentially [10]. One of the most popular among various group detection techniques is group decision feedback MUD, an extension from BLAST. This receiver detects one MIMO system at a time and then feeds the tentative decisions to other group detectors for interference cancellation. Although successive interference cancellation is asymptotically optimal under the assumption of perfect interference cancellation, it is very susceptible to inaccurate channel estimates, and also is currently deemed too complex for low-power mobile units. 4) Comments on Channel Information for Interfering Base Stations: An important shortcom- ing of the above techniques is that most require channel information for the neighboring base stations, which is traditionally not available. In 3G cellular systems such as HSDPA and EV- DV, mobile stations periodically monitor pilot channels of neighboring base stations to assist with handoff. These pilot signals could also be used to gather the required channel knowledge for the above multiuser receivers. For 3G CDMA systems, a parallel searcher structure might help to acquire these signals but it would necessarily increase the complexity and the power April 29, 2005 DRAFT
  8. 8. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 8 consumption of the mobile stations. C. Advanced Transmitter Techniques For many systems, in both theory and practice, transmitter adaptation has been shown to be a highly profitable means of exploiting channel conditions. A well-known example is adaptive modulation, which is used in EV-DO and HDSPA to achieve high data rates for users with good channels. Another method is the closed-loop diversity mode of WCDMA, which approximates the maximum ratio transmission beamforming vector. Transmitter-based techniques have the additional merit in a cellular downlink of transferring the complexity burden from the mobile unit to the base station, where higher complexity is more tolerable. In this section, we consider methods of using channel knowledge at the transmitter to help suppress other-cell interference using advanced signal processing techniques. 1) Cooperative Encoding: If the received interference signals are known to the transmitters, cooperative encoding among neighboring base stations can suppress other-cell interference. Such a joint encoding scheme is an example of so-called dirty paper coding (DPC), which has been shown to achieve the (maximum theoretical) capacity of the multiuser MIMO downlink channel [14]. However, joint encoding is nearly impossible to achieve in practical systems because it requires precise time and phase synchronization of the signals transmitted from multiple base stations, and exact channel knowledge at all the transmitters. Even though the 3G cellular systems have base station controllers (BSC) or radio network controllers (RNC), which control multiple base stations or node B’s, the precise accuracy required for DPC renders this technique as a theoretical upper bound, rather than a practical solution. 2) Closed-Loop MIMO Diversity Schemes: Given Mt > Mr antennas, it is possible to improve the performance of spatial multiplexing by using channel state information at the transmitter. One example is antenna subset selection where the best Mr of Mt antennas are selected. Other examples include eigenbeamforming and transmit precoding. These schemes require knowledge April 29, 2005 DRAFT
  9. 9. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 9 of channel state information at the transmitter; efficient limited feedback strategies for this purpose have already been developed [15]. Closed-loop diversity schemes provide a diversity improvement of Mt − Mr + 1 and additional array gain, which reduces the required transmit power for spatial multiplexing, and hence the interference caused to neighboring cells. On a system-wide level, such approaches have about the same impact on OCI as transmit diversity. 3) MIMO Combined with Transmit Beamforming: While the name “beamforming” has been applied to a large number of diverse techniques, the common idea is to use signal processing at the transmitter to maximize the signal energy sent to the desired user, while minimizing the interfer- ence sent towards interfering users. Some of these techniques include Space Division Multiple Access (SDMA), eigenmode beamforming for high capacity, and maximal ratio transmission for diversity [16]. Beamforming can be combined with spatial multiplexing to give multiple high data rate streams, although the dimensions used for beamforming do reduce the number of simultaneous data streams that can be transmitted. Although beamforming is traditionally envisioned to battle self-cell interference since it requires the complete interference statistics for each user at the transmitter, in doing so it reduces the other-cell interference. Despite its technical merits, beamforming has not found commercial adoption due to its requirement for rich channel knowledge at the transmitter. V. S TRATEGIC A PPROACHES TO THE OCI P ROBLEM As discussed in the prior section and summarized in Table VII, the numerous recently proposed methods for mitigating OCI in cellular MIMO systems have some important shortcomings when viewed in a practical context such as a 3G cellular system. Although some of these techniques have important merits and are being actively researched and considered, it is important to consider strategic approaches to handling OCI in cellular systems that do not require real- time information about the other-cell interference. In this section, we propose two system-level techniques that can in principle be combined with the previously discussed advanced signal April 29, 2005 DRAFT
  10. 10. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 10 processing algorithms to yield even larger gains, if and when these become viable. While these two techniques will effectively reduce OCI and increase spectral efficiency for any interference- limited cellular system, the gains are particularly impressive for multicell MIMO-CDMA due to its severe degradation in the face of OCI. A. Cooperatively Scheduled Transmission Recent work by the authors and others [17] has investigated the possibility for neighboring base stations to cooperatively schedule their transmission, a generalization of the concept of spatial frequency reuse. This scheduling could either be dynamic (and hence require simple intercell coordination) or pre-determined based on a universally shared time-hopping sequence. Just as frequency reuse achieves OCI reduction at the expense of decreasing spectral efficiency by the frequency reuse factor, cooperatively scheduled transmission reduces OCI at the expense of decreasing throughput by the transmit duty cycle. There are two important advantages of cooperatively scheduled transmission relative to traditional frequency reuse. First, universal frequency reuse can still be adopted, achieving an interference-averaging effect, simplifying frequency planning in deployment, and reducing the number of required frequency channels for the system. Second, cooperatively scheduled transmission achieves an additional gain termed expanded multiuser diversity if straightforward opportunistic scheduling is employed among neighboring base stations. For a 2x2 MIMO system with cooperatively scheduled transmission, the capacity gain of cooperatively scheduled transmission over a traditional frequency reuse system (f = 7) is shown in Figure 3, where all the users in each cell are assumed to be uniformly distributed and 2 × 2 Rayleigh MIMO channels are considered. As it is expected, cooperatively scheduled transmission exploits expanded multiuser diversity and achieves higher capacity than traditional frequency reuse (f = 7). The expanded multiuser diversity gain in terms of capacity is about 1.5 bps/Hz. It should be stressed however that the main motivation for introducing cooperatively scheduled April 29, 2005 DRAFT
  11. 11. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 11 transmission is not to increase the overall capacity relative to universal time and frequency reuse, but to support the many users that are near cell boundaries, so they can achieve acceptable data rates. B. Distributed antenna architecture Another strategic approach to reducing the other-cell interference problem is to adopt a distributed antenna architecture, which has been considered in the past as an effective means of extending coverage, eliminating dead spots, and lowering the blocking probability. In such an architecture, antenna modules are geographically distributed throughout the original cell to reduce the access distance for each node, and is then connected to a home base station (or central unit) via dedicated wires, fiber optics, or an exclusive RF link. An example of the distributed antenna cellular structure is given in Figure 4. Despite the increased equipment costs associated with this architecture, simple examples are already being widely deployed in 3G cellular systems by equipment manufacturers and service providers such as Korea Telecom, ADC, SK Telecom, and Cingular, due to the aforementioned qualities and also the ease of installation and maintenance. Since a distributed antenna architecture lowers the aggregate transmit power, it is natural that such a scheme will lower the amount of interference caused to neighboring cells. By transmitting to each user from the antenna closest to it (or more precisely, with the best channel), a form of macroscopic spatial diversity is also introduced to the system, with the net result being a much higher average received SINR, with particularly large gains for users near the distributed antennas (and hence the cell edges). Figure 5 shows the average SINR of a distributed antenna system with various transmission schemes when the target mobile stations are uniformly distributed. As can be seen, the SINR gain is quite dramatic, on the order of 15dB [18]. Although this preliminary result was only for a single antenna system (i.e. Mr = Mt = 1), it is expected that the SINR gains will be similar for a MIMO system. The large SINR gain achieved from deploying distributed antennas could be the key to the success of large-scale MIMO cellular April 29, 2005 DRAFT
  12. 12. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 12 systems with universal frequency reuse. VI. T HE F UTURE OF MIMO IN 3G C ELLULAR This article has attempted to establish that one of the key challenges facing the deployment of MIMO technology in 3G cellular networks is the sensitivity of MIMO receivers to interference. Since cellular systems are inherently interference-limited, this introduces a fundamental conflict. On one hand, system designs should minimize transmit power and rate in order to reduce the interference caused to neighboring cells. On the other hand, MIMO systems by nature increases the amount of data transmitted and hence requires a larger received SINR. Recent research approaches to this difficult problem have focused on advanced signal process- ing techniques at the receiver and transmitter as a means of reducing or cancelling the perceived interference. However, as this article has documented, most of these techniques suffer from important practical shortcomings in terms of complexity and required channel information that make their successful application to 3G cellular systems unlikely in the near to medium term. As an alternative, this article has proposed two simpler network-level approaches that require very little channel knowledge and effectively reduce other-cell interference through macro-diversity. Allowing for some simple back-channel communication among neighboring base stations, as is typically the case due to the need to coordinate handoffs and other operations, cooperatively scheduled transmission can help reduce the interference and increase the net spectral efficiency. Even larger gains are possible with a distributed antenna architecture, which is already being widely considered as a means to cheaply extend coverage and furnish additional capacity. Distributed antennas allow the transmit power to be dramatically lowered, which increases the average net received SINR by over an order of magnitude. While this improved SINR will benefit the capacity of any cellular network, the gain will be particularly large in a MIMO system due to its sensitivity to interference. April 29, 2005 DRAFT
  13. 13. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 13 VII. ACKNOWLEDGEMENTS The authors gratefully acknowledge feedback and input from Arunabha Ghosh (SBC Labs), Eko Onggosanusi (Texas Instruments), Steve Yi (Solid Technologies), and Huaiyu Dai (N.C. State). R EFERENCES [1] S. Alamouti, “A simple transmit diversity technique for wireless communications,” IEEE Journal on Sel. Areas in Communications, vol. 16, no. 8, pp. 1451–1458, Oct 1998. [2] V. Tarokh, N. Seshadri, and A. Calderbank, “Space-time codes for high data rate wireless communications: Performance criterion and code construction,” IEEE Trans. on Info. Theory, vol. 44, no. 2, pp. 744–765, Mar 1998. [3] R. Derryberry, S. Gray, D. Ionescu, G. Mandyam, and B. Raghothaman, “Transmit diversity in 3G CDMA systems,” IEEE Communications Magazine, vol. 40, no. 4, pp. 68–75, Apr. 2002. [4] E. Teletar, “Capacity of multi-antenna Gaussian channels,” European Trans. Telecommun., vol. 6, pp. 585–595, Nov.-Dec. 1999. [5] R. Heath, Jr., S. Sandhu, and A. Paulraj, “Antenna selection for spatial multiplexing systems with linear receivers,” IEEE Communications Letters, vol. 5, no. 4, pp. 142–144, Apr. 2001. [6] D. Gesbert, M. Shafi, D. Shiu, P. Smith, and A. Naguib, “From theory to practice: An overview of MIMO space-time coded wireless systems,” IEEE Journal on Sel. Areas in Communications, vol. 21, no. 3, pp. 281–302, Apr. 2003. [7] 3GPP2 C00AIE-20050310-021R1, “Hitachi technical approaches for future RAN evolution,” Mar. 2005. [8] 3GPP TR 25.876, “Multiple input multiple output antenna processing for HSDPA,” 2001-2011. [9] S. Catreux, P. F. Driessen, and L. J. Greenstein, “Attainable throughput of an interference-limited multiple-input multiple- output (MIMO) cellular system,” IEEE Trans. on Communications, vol. 49, no. 8, pp. 1307–1311, Aug. 2001. [10] H. Dai, A. Molisch, and H. Poor, “Downlink capacity of interference-limited MIMO systems with joint detection,” IEEE Trans. on Wireless Communications, vol. 3, no. 2, pp. 442–453, Mar. 2004. [11] W. Choi and J. G. Andrews, “On spatial multiplexing in cellular MIMO-CDMA systems with linear receivers,” in Proc., IEEE Intl. Conf. on Communications, Seoul, Korea, May 2005. [12] A. Burg, M. Borgmann, C. S. Claude, M. Wenk, M. Zellweger, and W. Fichtner, “Performance tradeoffs in the VLSI implementation of the sphere decoding algorithm,” in IEE 3G Mobile Communication Technologies Conference, Oct. 2004. [13] J. G. Andrews, “Inteference cancellation for celluar systems: A contemporary overview,” IEEE Wireless Communications Magazine, vol. 12, no. 2, pp. 19–29, Apr. 2005. April 29, 2005 DRAFT
  14. 14. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 14 [14] A. Goldsmith, S. Jafar, N. Jindal, and S. Vishwanath, “Capacity limits of MIMO channels,” IEEE Journal on Sel. Areas in Communications, vol. 21, no. 5, pp. 684–702, June 2003. [15] D. J. Love, R. W. Heath, W. Santipach, and M. L. Honig, “What is the value of limited feedback for MIMO channels?” IEEE Communications Magazine, vol. 42, no. 10, pp. 54–59, Oct. 2004. [16] A. Paulraj, R. Nabar, and D. Gore, Introduction to Space-Time Wireless Communications. Cambridge, UK: Cambridge, 2003. [17] H. Zhang and H. Dai, “Co-channel interference mitigation and cooperative processing in downlink multicell multiuser MIMO networks,” European Journal on Wireless Communications and Networking, 4th Quarter 2004. [18] W. Choi and J. G. Andrews, “Downlink performance and capacity of distributed antenna systems in a multicell environment,” Submitted to IEEE Trans. on Wireless Comm., Mar. 2005, available at∼jandrews/publications.html. Jeffrey G. Andrews received the B.S. with High Distinction from Harvey Mudd College in 1995, and the M.S. and Ph.D. in Electrical Engineering from Stanford University in 1999 and 2002, respectively. He is presently an Assistant Professor of Electrical and Computer Engineering at the University of Texas at Austin, in the Wireless Networking and Communications Group (WNCG). He helped develop Code Division Multiple Access (CDMA) systems as an engineer at Qualcomm from 1995 to 1997, and has served as a frequent consultant on communication systems to numerous corporations, startups, and government agencies. He is an associate editor for the IEEE Transactions on Wireless Communications. Wan Choi received the B. Sc. and M. Sc. degrees in Electrical Engineering from Seoul National University in 1996 and 1998, respectively. He was a Senior Member of the Technical Staff at the R&D Division of Korea Telecom (KT) Freetel from 1998 to 2003 where he researched 3G CDMA systems. He is currently pursuing the Ph.D. at the University of Texas at Austin. In 2001, in recognition of his research on downlink capacity for multicell CDMA systems, he received the IEEE Vehicular Technology Society Jack Neubauer Memorial Award, which recognizes the best system paper published in the IEEE Transactions on Vehicular Technology. His current research April 29, 2005 DRAFT
  15. 15. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 15 focuses on multicell MIMO communication. Robert W. Heath Jr. received B.S. and M.S. degrees from the University of Virginia, Char- lottesville, in 1996 and 1997, respectively, and a Ph.D. from Stanford University, California, in 2002, all in electrical engineering. From 1998 to 1999 he was a senior member of technical staff at Iospan Wireless mc, San Jose, California, where he played a key role in the design and implementation of the first commercial MIMO-OFDM communication system. From 1999 to 2001 he served as a senior consultant for Iospan Wireless Inc. Since January 2002 he has been with the Department of Electrical and Computer Engineering at the University of Texas at Austin where he is an assistant professor as part of the Wireless Networking and Communications Group. He serves as an Associate Editor for IEEE Transactions on Vehicular Technology. TABLE I H YPOTHETICAL P EAK DATA R ATES WITH MIMO IN 3GPP (Mr , Mt ) Code rate Modulation Data Rate/sub-stream Spreading Factor Total # of sub-streams Data rate (1,1) 3/4 64QAM 540 kbps 32 20 10.8 Mbps (2,2) 3/4 16QAM 360 kbps 32 40 14.4 Mbps (2,2) 3/4 QPSK 180 kbps 32 80 14.4 Mbps (4,4) 3/4 8PSK 540 kbps 16 80 21.6 Mbps April 29, 2005 DRAFT
  16. 16. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 16 TABLE II SUMMARY OF POSSIBLE OTHER-CELL INTERFERENCE (OCI) MITIGATION TECHNIQUES Technique Benefit Key shortcomings Fixable or fundamental? Frequency reuse Reduce OCI very simply Low spectral efficiency, Fundamental and effectively frequency planning Maximum Optimum co-reception of Very high complexity, OCI- Moore’s law will help Likelihood MUD signal and interference awareness complexity, but prohibitive in near future MMSE MUD Suppresses OCI with much Requires awareness of OCI, Requires instantaneous OCI lower complexity than ML many mobile antennas. knowledge, under present Simpler versions have only investigation by industry modest performance gain. OCI-blind MMSE Like ZF spatial receiver Enhances OCI rather than Fundamental, will provide only with lower noise suppressing it: very poor incremental gain enhancement performance Other-cell Good performance vs. Complexity still high, Promising in long-term w/ interference complexity awareness and accuracy of Moore’s law and future research cancellation OCI knowledge crucial Cooperative Optimal performance in Requires very accurate Unlikely to be practical in Encoding, i.e. Dirty theory channel knowledge and real- foreseeable future, if ever. Paper Coding time inter-cell coordination Closed-loop MIMO Achieves optimum diversity Sacrifices spatial dimensions Fundamental, but diversity diversity performance for multiplexing, channels techniques can lower OCI known at Tx somewhat Beamforming Reduces OCI Sacrifices spatial dimensions, Has important merits, but channels known at Tx implementation difficulties Cooperative Reduce OCI, multiuser Requires simple cooperation Feasible in the short-term Transmission diversity gain relative to between base stations frequency reuse Distributed Antenna Reduce OCI through Requires new infrastructure Feasible in the short-term with Systems lowered transmit power; deployment paradigm large infrastructure investment better coverage; ease of maintenance April 29, 2005 DRAFT
  17. 17. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 17 M t Antennas Interfering BS M r receive antennas M t Antennas M t interfering signals Mobile Station Home BS M t desired signals M t Antennas Interfering BS Fig. 1. Other-cell interference in MIMO cellular systems April 29, 2005 DRAFT
  18. 18. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 18 0 10 −1 10 Outage probability (Pout) −2 10 −3 10 Conventional CDMA system −4 2X2 MIMO−CDMA system w/ ZF filters 10 4X4 MIMO−CDMA system w/ ZF filters 8X8 MIMO−CDMA system w/ ZF filters 12X12 MIMO−CDMA system w/ ZF filters −5 10 0 5 10 15 20 25 30 Number of simultaneous users (K) Fig. 2. Outage probability of cellular MIMO-CDMA systems versus the number of simultaneous users with a zero-forcing spatial receiver. The noise enhancement is severe. 7 6.5 6 5.5 Capacity (bps/Hz) 5 4.5 4 3.5 3 Cooperatively scheduled transmission 2.5 Traditional frequency reuse (f=7) 2 10 20 30 40 50 60 70 80 90 100 Total numer of users in a cell Fig. 3. Shannon capacity of TDMA systems with cooperatively scheduled transmission and with frequency reuse (f = 7) vs. the number of users K. The expanded multiuser diversity gain from cooperatively scheduled transmission is about log log(K), helps compensate for the loss of spectral efficiency by the transmission duty cycle. April 29, 2005 DRAFT
  19. 19. SUBMISSION FOR IEEE COMMUNICATIONS MAGAZINE 19 R2 R3 BS R2 R4 R1 R3 6 R2 BS R3 R5 R6 R4 R1 1 BS R4 R1 R2 R5 R6 5 R3 W R5 R6 BS R2 R4 R1 R3 0 R2 BS R3 R5 R6 R4 R1 2 BS R2 R5 R6 R4 R1 R3 4 BS R5 R6 R4 R1 3 R5 R6 BS Base : station R Remote : Anteannas Module (RNA) W : Example worst case location Fig. 4. An example of a distributed antenna cellular architecture for 7 cells, each serviced by 7 total antennas – one at the original base station and 6 remote antenna modules that are spread throughout the cell. 35 Conventional cellular system DAS w/ blanket transmission scheme 30 DAS w/ single transmit selection scheme DAS w/ dual transmit selection scheme 25 Average SINR (dB) 20 15 10 5 0 2.5 3 3.5 4 Pathloss exponent Fig. 5. Average SINR versus the pathloss exponent for one transmit and receive antenna. The SINR gain from distributed antenna systems is dramatic, which will increase the feasibility of cellular MIMO with universal frequency reuse. April 29, 2005 DRAFT