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Erschienen in:
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2014 | OriginalPaper | Buchkapitel

6. Feedback Strategies for Multiantenna Multiuser Systems

verfasst von : Berna Özbek, Didier Le Ruyet

Erschienen in: Feedback Strategies for Wireless Communication

Verlag: Springer New York

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Abstract

In this chapter, we focus on the different feedback strategies for the case of a multiuser wireless communication system with multiple transmitter and receiver antennas. Firstly, we analyze the capacity of the multiuser MIMO system with single receive antenna for uplink and downlink by assuming that the full channel state information (CSI) is available at the transmitter. Secondly, we examine the precoding and user selection algorithms for MIMO multiuser systems with one and multiple receive antennas in flat fading and frequency selective wireless channels. Since optimal precoding using dirty paper coding has a prohibitively high computational complexity due to the associated encoding process, it is a great practical interest to design MIMO multiuser systems with low complexity and a minimum CSI requirement at the transmitter side. One suboptimal approach is to apply linear precoding schemes, such as zero forcing beamforming (ZF-BF) or minimum mean square error criterion. Multiuser MIMO wireless communication with ZF-BF requires a brute-force exhaustive search over all possible user sets and the complexity of an exhaustive search is prohibitive when the number of users is large. In order to decrease the complexity of this search, several suboptimal user scheduling algorithms have been designed. Generally, these algorithms fall into two categories: Capacity-based and Frobenius norm-based algorithm. Lastly, we show the effect of reduced and limited feedback information including user selection at the receiver side and quantization for both single carrier and multicarrier transmissions. We perform user selection at the user side since the users having a poor channel (low norm or/and interference) should not take part in the user selection algorithm, nor feedback their channel information. By using a self-discrimination criterion at the receiver side, it is possible to reduce the feedback load and the complexity of the user selection algorithm at base station. We show different user selection criteria and quantization strategies to reduce feedback load for both single and multicarrier communication systems.

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Literatur
1.
Caire G, Shamai S (2003) On the achievable throughput of a multi antenna gaussian broadcast channel. IEEE Trans. on Information Theory. 43: 1691–1706.MathSciNetCrossRef
2.
Costa M H R (1983) Writing on Dirty Paper. IEEE Trans. on Info. Theory.29: 439–441.CrossRefMATH
3.
Yu W, Cioffi J (2004). Trellis and convolutional precoding for transmitter based interference pre-subtraction. IEEE Trans. on Communications. 53: 1220–1230.CrossRef
4.
Choi L U, Murch R D (2004). A transmit preprocessing technique for multiuser MIMO systems using a decomposition approach. IEEE Trans. Wireless Commun. 3: 20–24.CrossRef
5.
Christensen S S, Agarwal R, de Carvalho E, J.M. Cioffi (2009). Weighted sum-rate maximization using weighted mmse for MIMO-BC beamforming design. In Proc. IEEE International Conference on Communications, Germany.
6.
Choi W, Forenza A, Andrews J G, Heath R W (2007). Opportunistic space-division multiple access with beam selection. IEEE Trans. Commun., vol. 55: 2371–2380.
7.
Castro P M, Joham M, Castedo L, Utschick W (2007). Robust Precoding for Multi-User MISO Systems with Limited-Feedback Channels. In International ITG Workshop in Smart Antennas, Germany.
8.
Chung J, Hwang C-S, Kim K, Kim Y K (2003). A Random Beamforming Technique in MIMO Systems Exploiting Multiuser Diversity. IEEE Journal on Selected Areas in Communication. 21: 848–855.CrossRef
9.
Diaz J, Simeone O, Bar-Ness Y (2006). Sum-Rate of MIMO Broadcast Channels with One Bit Feedback. In Proc. of Int. Symp. Inform. Theory (ISIT), USA, 1944–1948.
10.
Dimic G, Sidiropoulos N D (2005). On Downlink Beamforming With Greedy User Selection: Performance Analysis and a Simple New Algorithm. IEEE Trans. on Signal Processing. 53: 3857–3868.MathSciNetCrossRef
11.
Gesbert D, Alouini M S (2004). How much feedback is multi-user diversity really worth? In Proc. IEEE International Conference on Communications, France.
12.
Harashima H, Miyakawa H (1972). Matched-transmission technique for channels with intersymbol interference. IEEE Trans. Communication. 20: 774 –780.CrossRef
13.
Huang K, Andrews J G, Heath R W (2009). Performance of Orthogonal Beamforming for SDMA with Limited Feedback. IEEE Trans. on Vehicular Technology, 58: 152–164.CrossRef
14.
Hochwald B M, Peel C B, Swindlehurst A L (2005). A vector-perturbation technique for near-capacity multiantenna multiuser communication - Part II: Perturbation. IEEE Trans. Communication. 53: 537–544.CrossRef
15.
Sharif M, Hassibi B (2005). On the capacity of MIMO Broadcast channels with partial side information. IEEE Trans. on Information Theory. 51: 506–522.MathSciNetCrossRef
16.
Huang K, Heath R W, Andrews J. (2007). Multi-user aware limited feedback for MIMO systems. submitted to IEEE Transactions on Signal Processing.
17.
Hoydis J, ten Brink S, Debbah M (2011). Massive MIMO: how many antennas do we need? Proceedings of Allerton Conference on Communication, Control, and Computing.
18.
Jindal N, Vishwanath S, Goldsmith A (2004). On the duality of Gaussian multiple-access and broadcast channels. IEEE Trans.on Information Theory. 50: 768–783.
19.
Jindal N, Goldsmith A (2005). Dirty paper coding vs. TDMA for MIMO broadcast channel. IEEE Trans. on Information Theory. 51: 1783–1794.MathSciNetCrossRef
20.
Jindal N (2006). MIMO broadcast channels with finite-rate feedback. IEEE Transactions on Information Theory. 52: 5045–5060.MathSciNetCrossRef
21.
Cadambe V R, Jafar S A (2008) Interference Alignment and Degrees of Freedom of the K-User Interference Channel.IEEE Transactions on Information Theory. 54:3425–3441.
22.
Kim I, Hong S, Chassemzadeh S, Tarokh V (2005). Optimum opportunistic beamforming based on multiple weighting vectors. IEEE Trans. on Wireless Communication. 4: 2683–2687.CrossRef
23.
Kim J S, Kim H, Park CS, Lee K B (2006). On the performance of multiuser MIMO systems in WCDMA/HSDPA: Beamforming, feedback and user diversity. IEICE Trans. on Communication, 89: 2161–2169.CrossRef
24.
Khanfir H, Le Ruyet D, Ozbek B (2007). Semi-orthogonal user selection for MIMO systems with quantized feedback. ITG/IEEE workshop on smart antennas. Germany.
25.
Khanfir H, Le Ruyet D, Ozbek B (2012). Reduced feedback load using User Selection Algorithms for the Multiuser MISO Systems. Wiley Transactions on Emerging Telecommunications. 63: 295–305.
26.
Ko K, Lee J (2012). Multiuser MIMO User Selection Based on Chordal Distance. IEEE Trans. on Communication. 60: 649–654.CrossRef
27.
Lee J, Jindal N (2006). Dirty paper coding vs. linear precoding for MIMO broadcast channels. In Proc. 2006 Asilomar Conf. Signal, Syst. Comp.
28.
29.
Maddah-Ali M A, Motahari S A, Khandani, A K (2008) Communication Over MIMO X Channels: Interference Alignment,Decomposition, and Performance Analysis. IEEE Trans. Information Theory.54: 595–602.
30.
Marzetta T L (2006). How much training is required for multiuser MIMO? In proceedings of IEEE Asilomar Conference on Signals, Systems, and Computers.
31.
Marzetta T L (2010). Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Transactions on Wireless Communications, 9: 3590–3600.CrossRef
32.
Mukkavilli K K, Sabarwal A, Erkip E, Aazhang B (2003). On beamforming with finite rate feedback in multiple antenna systems. IEEE Trans. on Information Theory. 49: 2562–2579.CrossRef
33.
Ngo H Q, Larsson E G, Marzetta T L (2011). Uplink power efficiency of multiuser MIMO with very large antenna arrays. In Proceedings of Allerton Conference on Communication, Control, and Computing.
34.
Ozbek B, Le Ruyet D (2009). Reduced feedback designs for SDMA-OFDMA systems. In Proc. of IEEE International Communication Conference (ICC),Dresden.
35.
Ozbek B, Le Ruyet D (2012). Feedback channel designs for fair scheduling in MISO-OFDMA systems. EURASIP Journal on Wireless Communications and Networking 2012, 2012:220 doi:10.1186/1687-1499-2012-220.CrossRef
36.
Peel C, Hochwald B, Lee Swindlehurst A (2003). A vector perturbation technique for near capacity multi-antenna multi-user communication. IEEE Trans. on Communications. 53: 537–544.
37.
Raghavan V, Jr Heath R W, Sayeed A M (2007). Systematic Codebook Designs for Quantized Beamforming in Correlated MIMO Channels. IEEE Journal on Selected Areas in Communications. 25: 1298–1310.
38.
UE PMI Feedback Signalling for User Pairing-Coordination Alcatel-Lucent, R1-083759, R1-084141, R1-090051, R1-090777.
39.
Best Companion Reporting for Improved Single-Cell MU-MIMO Pairing Alcatel-Lucent, R1-090926, R1-091307, R1-092031,R1-092546, R1-093333.
40.
Rhee W, Yu W, Cioffi J M (2004). The optimality of beamforming in uplink multiuser wireless systems. IEEE Trans. on Wireless Communications. 3: 86–96.CrossRef
41.
Rusek F, Persson D, Lau B K, Larsson E G, Marzetta T L, Edfors O, Tufvesson F (2013). Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Processing Magazine, 30: 40–60.CrossRef
42.
Sharif M, Hassibi B (2007). A comparison of time-sharing, DPC, and beamforming for MIMO broadcast channels with many users. IEEE Trans. Communication. 55: 11–15.CrossRef
43.
Spencer Q H, Swindlehurst A L, Haardt M (2004). Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels. IEEE Trans. Signal Processing. 52: 461–471.MathSciNetCrossRef
44.
Swannack C, Uysal-Biyikoglu E, Wornell G W (2005). Finding NEMO: Near Orthogonal Sets and Applications to MIMO Broadcast Scheduling. In Int Conf on Wireless Networks, Communications and Mobile Computing. 2: 1035–1040.
45.
Peel C B, Hochwald B M, Swindlehurst A L (2005). A vector perturbation technique for near-capacity multiantenna multiuser communication-part i: channel inversion and regularization. IEEE Transactions on Communications. 53: 195–202.CrossRef
46.
Svedman P, Wilson S K, Cimini L J, Ottersten B (2007). Opportunistic Beamforming and Scheduling for OFDMA systems. IEEE Transaction on Communications. 55: 941–952.CrossRef
47.
Shen Z, Chen R, Andrews J G, Heath J R W, Evans B L (2006). Low complexity user selection algorithms for multiuser MIMO systems with block diagonalization. IEEE Trans. Signal Process. 54: 3658–3663.CrossRef
48.
So J, Cioffi J M (2009). Multiuser Diversity in a MIMO System with Opportunistic Feedback. IEEE Trans. on Vehicular Technology. 58: 4909–4918.CrossRef
49.
Slim I, Mezghani A, Nossek J A (2011). Quantized CDI Based Tomlinson Harashima Precoding for Broadcast Channels. In Proceedings of IEEE Intern. Conf. On Communications (ICC).
50.
Sun L, Mckay M (2010). Eigen-based transceivers for the MIMO broadcast channel with semi-orthogonal user selection. IEEE Trans. on Signal Processing. 58:5246–5261.MathSciNetCrossRef
51.
Tu Z, Blum R S (2003). Multiuser diversity for a dirty paper approach. IEEE Commun. Lett. 7:370–372.CrossRef
52.
Tomlinson M (1971). New automatic equalizer employing modulo arithmetic. Electronic Letters 7: 138–139.CrossRef
53.
Xu J, Andrews JG, Jafar S A (2012). Broadcast Channels with Delayed Finite-Rate Feedback: Predict or Observe? IEEE Trans. on Wireless Communications. 11: 1456–1467.CrossRef
54.
Viswanath P, Tse DNC, Laroia R (2002). Opportunistic beamforming using dumb antennas. IEEE Trans. on Inform. Theory. 48:1277–1294.MathSciNetCrossRefMATH
55.
Viswanath P, Tse D (2003). Sum capacity of the vector gaussian broadcast channel and uplink downlink duality. IEEE Trans. on Information Theory. 49:1912–1921.MathSciNetCrossRef
56.
Vishwanath S, Jindal N, Goldsmith A (2003). Duality, achievable rates and sum rate capacity of Gaussian MIMO broadcast channel. IEEE Trans.on Information Theory. 49: 2658–2668.
57.
Wu Y, Zhang J, Xu M, Zhou S, Xu X (2005). Multiuser MIMO downlink precoder design based on the maximal SJNR criterion. In Proc. IEEE Global Telecommunications Conference. 5: 2698–2703.
58.
Yu W, Cioffi J (2002). Sum Capacity of Gaussian Vector Broadcast Channels. In Proc. of Int. Symp. Information Theory.
59.
Yu W, Cioffi J (2004) Sum capacity of gaussian vector broadcast channels. IEEE Trans. on Info. Theory. 50:1875–1892.MathSciNetCrossRef
60.
Yoo T, Goldsmith A (2006). On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming. IEEE J. on Select. Areas in Commun. 24:528–541.CrossRef
61.
Zhou H, Zhang J, Xue J, Zhang Y, Tian J, Power K, Vadgama S, Nakatsugawa K (2008). Proposal for User Grouping Scheme for MU-MIMO. IEEE 802.16m-08392.
62.
Zamir R, Shamai S, Erez U (2002). Nested Linear Lattice Codes for Structured Multiterminal Binning. IEEE Trans. on Info. Theory. 19:1250–1276.MathSciNetCrossRef
Metadaten
Titel
Feedback Strategies for Multiantenna Multiuser Systems
verfasst von
Berna Özbek
Didier Le Ruyet
Copyright-Jahr
2014
Verlag
Springer New York
Buch
Feedback Strategies for Wireless Communication

Print ISBN: 978-1-4614-7740-2

Electronic ISBN: 978-1-4614-7741-9

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-1-4614-7741-9_6

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