Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO

IEEE Transactions on Signal Processing

Volumn 63, Issue 23, 2015, Pages 6169-6183

  Gao, Zhen ^a   Dai, Linglong ^a   Wang, Zhaocheng ^a   Chen, Sheng ^b,c  

^a TSINGHUA UNIVERSITY   (China)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^c KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

Channel estimation; compressive sensing; feedback; frequency division duplex; massive multi input multi output; spatially common sparsity; temporal correlation

Indexed keywords

CHANNEL STATE INFORMATION; COMMUNICATION CHANNELS (INFORMATION THEORY); COMPRESSED SENSING; DESIGN; FEEDBACK; FREQUENCY ESTIMATION; MIMO SYSTEMS; SIGNAL RECONSTRUCTION;

COMPRESSIVE SENSING; FREQUENCY-DIVISION DUPLEXES; MULTI INPUT MULTI OUTPUT; SPATIALLY COMMON SPARSITY; TEMPORAL CORRELATIONS;

CHANNEL ESTIMATION;

EID: 84960145900     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2015.2463260     Document Type: Article

References (28)

1. L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, R. Zhang, "An overview of massive MIMO: Benefits and challenges," IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 742-758, Oct. 2014.
2. F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, F. Tufvesson, T. L. Marzetta, "Scaling up MIMO: Opportunities and challenges with very large arrays," IEEE Signal Process. Mag., vol. 30, no. 1, pp. 40-60, Jan. 2013.
3. J. Hoydis, S. t. Brink, 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.
4. H. Yin, D. Gesbert, M. Filippou, Y. Liu, "A coordinated approach to channel estimation in large-scale multiple-antenna systems," IEEE J. Sel. Areas Commun., vol. 31, no. 2, pp. 264-273, Feb. 2013.
5. J. Choi, D. J. Love, P. Bidigare, "Downlink training techniques for FDD massive MIMO systems: Open-loop and closed-loop training with memory," IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 802-814, Oct. 2014.
6. L. Dai, Z. Wang, Z. Yang, "Spectrally efficient time-frequency training OFDM for mobile large-scale MIMO systems," IEEE J. Sel. Areas Commun., vol. 31, no. 2, pp. 251-263, Feb. 2013.
7. D. Angelosante, E. Biglieri, M. Lops, "Sequential estimation of multipath MIMO-OFDM channels," IEEE Trans. Signal Process., vol. 57, no. 8, pp. 3167-3181, Aug. 2009.
8. M. Simko, P. S. R. Diniz, Q. Wang, M. Rupp, "Adaptive pilotsymbol patterns for MIMO OFDM systems," IEEE Trans. Wireless Commun., vol. 12, no. 9, pp. 4705-4715, Sep. 2013.
9. Y.-H. Nam, Y. Akimoto, Y. Kim, M.-L. Lee, K. Bhattad, A. Ekpenyong, "Evolution of reference signals for LTE-advanced systems," IEEE Commun. Mag., vol. 50, no. 2, pp. 132-138, Feb. 2012.
10. H. Minn and N. Al-Dhahir, "Optimal training signals for MIMO OFDM channel estimation," IEEE Trans. Wireless Commun., vol. 5, no. 5, pp. 1158-1168, May 2006.
11. S. Noh, M. D. Zoltowski, Y. Sung, D. J. Love, "Pilot beam pattern design for channel estimation in massive MIMO systems," IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 787-801, Oct. 2014.
12. P. Cheng and Z. Chen, "Multidimensional compressive sensing based analog CSI feedback for massive MIMO-OFDM systems," in Proc. Veh. Technol. Conf. (VTC)-Fall 2014, Vancouver, Canada, Sep. 14-17, 2014, pp. 1-6.
13. P.-H. Kuo, H. T. Kung, P.-A. Ting, "Compressive sensing based channel feedback protocols for spatially-correlated massive antenna arrays," in Proc. WCNC, Shanghai, China, Apr. 1-4, 2012, pp. 492-497.
14. X. Rao, V. K. N. Lau, X. Kong, "CSIT estimation and feedback for FDD multi-user massive MIMO systems," in Proc. ICASSP, Florence, Italy, May 4-9, 2014, pp. 3157-3161.
15. X. Rao and V. K. N. Lau, "Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems," IEEE Trans. Signal Process., vol. 62, no. 12, pp. 3261-3271, Jun. 2014.
16. J. Nam, A. Adhikary, J.-Y. Ahn, G. Caire, "Joint spatial division and multiplexing: Opportunistic beamforming, user grouping and simplified downlink scheduling," IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 876-890, Oct. 2014.
17. A. Hu, T. Lv, H. Gao, Z. Zhang, S. Yang, "An ESPRIT-based approach for 2-D localization of incoherently distributed sources in massive MIMO systems," IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 996-1101, Oct. 2014.
18. Y. Zhou, M. Herdin, A. M. Sayeed, E. Bonek, "Experimental study of MIMO channel statistics and capacity via the virtual channel representation," Univ. Wisconsin-Madison, Madison, WI, USA, Tech. Rep, 2007.
19. D. Tse and P. Viswanath, Fundamentals ofWireless Communication. Cambridge, U.K.: Cambridge Univ. Press, 2005.
20. Mobile Broadband Multimedia Networks, Techniques, Models and Tools for 4G, L. M. Correia, Ed. San Diego, CA, USA: Academic, 2006.
21. Y. S. Cho, J. Kim, W. Y. Yang, C.-G Kang, MIMO-OFDMWireless Communications With MATLAB. Singapore: Wiley (Asia) Pte. Ltd., 2010.
22. T. T. Do, L. Gan, N. Nguyen, T. D. Tran, "Sparsity adaptive matching pursuit algorithm for practical compressed sensing," in Proc. 42nd Asilomar Conf. Signal, Syst., Comput., Pacific Grove, CA, USA, Oct. 26-29, 2008, pp. 581-587.
23. S. M. Kay, Fundamentals of Statistical Signal processing, Volume II: Detection Theory. Englwood Cliffs, NJ, USA: Prentice-Hall, 1998.
24. M. F. Duarte and Y. C. Eldar, "Structured compressed sensing: From theory to applications," IEEE Trans. Signal Process., vol. 59, no. 9, pp. 4053-4085, Sep. 2009.
25. P. Billingsley, Probability and Measure. New York, NY, USA: Wiley, 1979.
26. J. Chen and X. Huo, "Theoretical results on sparse representations of multiple-measurement vectors," IEEE Trans. Signal Process., vol. 54, no. 12, pp. 4634-4643, Dec. 2006.
27. A. Björck, Numerical Methods for Matrix Computations. New York, NY, USA: Springe International Publishing AG, 2014.
28. R. Couillet and M. Debbah, Random Matrix Methods forWireless Communications. Cambridge, U.K.: Cambridge Univ. Press, 2011.