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http://dx.doi.org/10.1109/JCN.2016.000012

Compressed Channel Feedback for Correlated Massive MIMO Systems  

Sim, Min Soo (School of Integrated Technology, Yonsei University)
Park, Jeonghun (Dept. of ECE, Univ. of Texas at Austin)
Chae, Chan-Byoung (School of Integrated Technology, Yonsei University)
Heath, Robert W. Jr. (Dept. of ECE, Univ. of Texas at Austin)
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Abstract
Massive multiple-input multiple-output (MIMO) is a promising approach for cellular communication due to its energy efficiency and high achievable data rate. These advantages, however, can be realized only when channel state information (CSI) is available at the transmitter. Since there are many antennas, CSI is too large to feed back without compression. To compress CSI, prior work has applied compressive sensing (CS) techniques and the fact that CSI can be sparsified. The adopted sparsifying bases fail, however, to reflect the spatial correlation and channel conditions or to be feasible in practice. In this paper, we propose a new sparsifying basis that reflects the long-term characteristics of the channel, and needs no change as long as the spatial correlation model does not change. We propose a new reconstruction algorithm for CS, and also suggest dimensionality reduction as a compression method. To feed back compressed CSI in practice, we propose a new codebook for the compressed channel quantization assuming no other-cell interference. Numerical results confirm that the proposed channel feedback mechanisms show better performance in point-to-point (single-user) and point-to-multi-point (multi-user) scenarios.
Keywords
MIMO system; multi-user system; channel feedback; compressed feedback;
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1 R. G. Baraniuk, "Compressive sensing," IEEE Signal Process Mag., vol. 24, no. 4, pp. 118-121, 2007.   DOI
2 Y. Linde, A. Buzo, and R. M. Gray, "An algorithm for vector quantizer design," IEEE Trans. Commun., vol. 28, no. 1, pp. 84-95, 1980.   DOI
3 H. Q. Ngo, E. G. Larsson, and T. L. Marzetta, "Energy and spectral efficiency of very large multiuser MIMO systems," IEEE Trans. Commun., vol. 61, no. 4, pp. 1436-1449, 2011.   DOI
4 M. S. Sim and C.-B. Chae, "Compressed channel feedback for correlated massive MIMO systems," in Proc. IEEE Globecom, 2014.
5 T. L. Marzetta, "How much training is required for multiuser MIMO?," in Proc. Asilomar Conf. on Signals, Systems and Computers, pp. 359-363, 2006.
6 F. Rusek et al., "Scaling up MIMO: Opportunities and challenges with very large arrays," IEEE Signal Process Mag., vol. 30, no. 1, pp. 40-60, Jan. 2013.   DOI
7 T. L.Marzetta, "Noncooperative cellular wireless with unlimited numbers of base station antennas," IEEE Trans. Wireless Commun., vol. 9, no. 11, pp. 3590-3600, Nov. 2010.   DOI
8 J. Hoydis, S. ten Brink, and M. Debbah, "Massive MIMO in the UL/DL of cellular networks: How many antennas do we need?," IEEE J. Sel. Areas Commun., vol. 31, no. 2, pp. 160-171, Feb. 2013.   DOI
9 D. Gesbert, M. Kountouris, R. W. Heath, Jr., C.-B. Chae, and T. Salzer, "Shifting the MIMO paradigm: From single user to multiuser communications," IEEE Signal Process Mag., vol. 24, no. 5, pp. 36-46, Oct. 2007.   DOI
10 N. Jindal, "MIMO broadcast channels with finite-rate feedback," IEEE Trans. Inf. Theory, vol. 52, no. 11, pp. 5045-5060, Nov. 2006.   DOI
11 P. Ding, D. J. Love, and M. D. Zoltowski, "Multiple antenna broadcast channels with shape feedback and limited feedback," IEEE Trans. Signal Process., vol. 55, no. 7, pp. 3417-3428, July 2007.   DOI
12 J. Nam, J.-Y Ahn, A. Adhikary, and G. Caire, "Joint spatial division and multiplexing: Realizing massive MIMO gains with limited channel state information," in Proc CISS, Mar. 2012, pp.1-6.
13 Y.-G. Lim, C.-B. Chae, and G. Caire, "Performance analysis of massive MIMO for cell-boundary users," Sept. 2013, submitted to IEEE Trans. Wireless Commun. [Online] Available: arXiv:1309.7817
14 B. Hassibi and B. M. Hochwald, "How much training is needed in multiple-antenna wireless links?," IEEE Trans. Inf. Theory, vol. 49, no. 4, pp. 951-963, Apr. 2003.   DOI
15 C. K. Au-Yeung and D. J. Love, "On the performance of random vector quantization limited feedback beamforming in a MISO system," IEEE Trans. Wireless Commun., vol. 6, no. 2, pp. 458-462, Feb. 2007.   DOI
16 J. Choi, Z. Chance, D. J. Love, and U. Madhow, "Noncoherent trellis coded quantization: A practical limited feedback technique for massive MIMO systems," IEEE Trans. Commun., vol. 61, no. 12, pp. 5016-5029, Dec. 2013.   DOI
17 C.-B. Chae, D. Mazzarese, N. Jindal, and R. W. Heath, Jr., "Coordinated beamforming with limited feedback in the MIMO broadcast channel," IEEE J. Sel. Areas Commun., vol. 26, no. 8, pp. 1505-1515, 2008.   DOI
18 C.-B. Chae, D. Mazzarese, T. Inoue, and R. W. Heath, Jr., "Coordinated beamforming for the multiuser MIMO broadcast channel with limited feedforward," IEEE Trans. Signal Process., vol. 56, no. 12, pp. 6044-6056, 2008.   DOI
19 C. K. Au-Yeung and D. J. Love, "On the performance of random vector quantization limited feedback beamforming in a MISO system," IEEE Trans. Wireless Commun., vol. 6, no. 2, pp. 458-462, Feb. 2007.   DOI
20 P.-H. Kuo, H. T. Kung, and P.-A. Ting, "Compressive sensing based channel feedback protocols for spatially-correlated massive antenna arrays," in Proc. IEEE Wireless Communication and Network Conf., 2012, pp. 492-497.
21 W. Santipach and M. L. Honig, "Asymptotic performance of MIMO wireless channels with limited feedback," in Proc. IEEE Military Communications Conf., Oct. 2003, vol. 1, pp. 141-146.
22 D. J. Love, R. W. Heath, Jr., and T. Strohmer, "Grassmannian beamforming for multiple-input multiple-output wireless systems," IEEE Trans. Inf. Theory, vol. 49, no. 10, pp. 2735-2747, Oct. 2003.   DOI
23 E. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489-509, 2006.   DOI
24 D. L. Donoho, "Compressed sensing," IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, 2006.   DOI
25 Spatial channel model for Multiple Input Multiple Output (MIMO) simulations, 3GPP TR 25.996 V6.1.0 Std., Sept. 2003.
26 A. Adhikary, J.-Y. Ahn J. Nam, and G. Caire, "Joint spatial division and multiplexing: The large-scale array regime," IEEE Trans. Inf. Theory, vol. 59, no. 10, pp. 6441-6463, Oct. 2013.   DOI
27 J. A. Tropp and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007.   DOI
28 A. Molisch, Wireless communications, Wiley, 2011.
29 A. Eriksson, P. Stoica, and T. Soderstrom, "On-line subspace algorithms for tracking moving sources," IEEE Trans. Signal Process., vol. 42, no. 9,pp. 2319-2330, Sept. 1994.   DOI
30 S. L. Loyka, "Channel capacity of MIMO architecture using the exponential correlation matrix," IEEE Commun. Lett., vol. 5, no. 9, pp. 369-371, Sept. 2001.   DOI
31 W. Dai and O. Milenkovic, "Subspace pursuit for compressive sensing signal reconstruction," IEEE Trans. Inf. Theory, vol. 55, no. 5, pp. 2230-2249, 2009.   DOI
32 R. Gray, "Vector quantization," IEEE ASSP Mag., pp. 4-29, 1984.
33 A. Gersho and R. M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Publishers, Norwell, MA, USA, 1991.
34 A. Edelman, "Eigenvalues and condition numbers of random matrices," SIAM J. Matrix Anal. Appl., vol. 9, no. 4, pp. 543-560, 1988.   DOI
35 T. Ratnarajah, R. Vaillancourt, and M. Alvo, "Eigenvalues and condition numbers of complex random matrices," SIAM J. Matrix Anal. Appl., vol. 26, no. 2, pp. 441-456, 2005.   DOI