Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems

IEEE Transactions on Signal Processing

Volumn 62, Issue 12, 2014, Pages 3261-3271

  Rao, Xiongbin ^a   Lau, Vincent K N ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Compressive sensing; CSIT estimation and feedback; joint orthogonal matching pursuit (J OMP); massive MIMO

Indexed keywords

RECOVERY; SIGNAL RECONSTRUCTION;

CLOSED-FORM EXPRESSION; COMPRESSIVE SENSING; ESTIMATION APPROACHES; MEAN ABSOLUTE ERROR; ORTHOGONAL MATCHING PURSUIT; PROPAGATION ENVIRONMENT; RECOVERY PERFORMANCE; SPATIAL MULTIPLEXING GAIN;

MIMO SYSTEMS;

EID: 84901396051     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2014.2324991     Document Type: Article

References (44)

1. E. Telatar, "Capacity of multi-antenna Gaussian channels," Euro. Trans. Telecommun., vol. 10, no. 6, pp. 585-595, 1999.
2. E. G. Larsson, F. Tufvesson, O. Edfors, and T. L. Marzetta, "Massive MIMO for next generation wireless systems," arXiv Preprint arXiv:1304.6690 2013 [Online]. Available: http://arxiv.org/abs/1304. 6690
3. Q. Shi,M. Razaviyayn, Z.-Q. Luo, and C. He, "An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel," IEEE Trans. Signal Process., vol. 59, no. 9, pp. 4331-4340, 2011.
4. T. E. Bogale and L. Vandendorpe, "Weighted sum rate optimization for downlink multiuser MIMO coordinated base station systems: Centralized and distributed algorithms," IEEE Trans. Signal Process., vol. 60, no. 4, pp. 1876-1889, 2012.
5. J.Hoydis, S. ten Brink, andM.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, 2013.
6. S. L. H. Nguyen and A. Ghrayeb, "Compressive sensing-based channel estimation for massivemultiuserMIMO systems," in Proc. IEEEWireless Commun. Netw. Conf. (WCNC), 2013, pp. 2890-2895.
7. P. W. Chan, E. S. Lo, R. R. Wang, E. K. Au, V. K. Lau, R. S. Cheng, W. H. Mow, R. D. Murch, and K. B. Letaief, "The evolution path of 4G networks: FDD or TDD?," IEEE Commun. Mag., vol. 44, no. 12, pp. 42-50, 2006.
8. T. L. Marzetta, G. Caire, M. Debbah, I. Chih-Lin, and S. K. Mohammed, Eds., J. Commun. Netw. (Special Issue on Massive MIMO), vol. 15, no. 4, pp. 333-337, 2013.
9. Q. Sun, D. C. Cox, H. C. Huang, and A. Lozano, "Estimation of continuous flat fading MIMO channels," IEEE Trans. Wireless Commun., vol. 1, no. 4, pp. 549-553, 2002.
10. M. Biguesh and A. B. Gershman, "Training-based MIMO channel estimation: A study of estimator tradeoffs and optimal training signals," IEEE Trans. Signal Process., vol. 54, no. 3, pp. 884-893, 2006.
11. H. Yin, D. Gesbert, M. Filippou, and 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, 2013.
12. Y. Zhou,M. Herdin, A. M. Sayeed, and E. Bonek, "Experimental study of MIMO channel statistics and capacity via the virtual channel representation," Univ.Wisconsin-Madison,Madison,WI, USA, Tech. Rep., Feb. 2007.
13. P. Kyritsi, D. C. Cox, R. A. Valenzuela, and P. W.Wolniansky, "Correlation analysis based onMIMO channel measurements in an indoor environment," IEEE J. Sel. Areas Commun., vol. 21, no. 5, pp. 713-720, 2003.
14. F. Kaltenberger, D. Gesbert, R. Knopp, and M. Kountouris, "Correlation and capacity of measured multi-user MIMO channels," in Proc. IEEE Int. Symp. Personal, Indoor, Mobile Radio Commun. (PIMRC), 2008, pp. 1-5.
15. J. Hoydis, C. Hoek, T.Wild, and S. ten Brink, "Channel measurements for large antenna arrays," in Proc. IEEE Int. Symp. Wireless Commun. Systems (ISWCS), 2012, pp. 811-815.
16. X. Gao, O. Edfors, F. Rusek, and F. Tufvesson, "Linear pre-coding performance in measured very-large MIMO channels," in Proc. IEEE Veh. Technol. Conf. (VTC), 2011, pp. 1-5.
17. C. R. Berger, Z. Wang, J. Huang, and S. Zhou, "Application of compressive sensing to sparse channel estimation," IEEE Commun. Mag., vol. 48, no. 11, pp. 164-174, 2010.
18. W. U. Bajwa, J. Haupt, A. M. Sayeed, and R. Nowak, "Compressed channel sensing: A new approach to estimating sparse multipath channels," in Proc. IEEE, 2010, vol. 98, no. 6, pp. 1058-1076.
19. J. Poutanen, K. Haneda, J. Salmi, V. Kolmonen, F. Tufvesson, T. Hult, and P. Vainikainen, "Significance of common scatterers in multi-link indoor radio wave propagation," in Proc. IEEE Eur. Conf. Antennas Propag. (EuCAP), 2010, pp. 1-5.
20. Y. Barbotin, A. Hormati, S. Rangan, and M. Vetterli, "Estimation of sparseMIMOchannels with common support," IEEE Trans. Commun., vol. 60, no. 12, pp. 3705-3716, Dec. 2012.
21. D. Baron,M. F. Duarte, M. B. Wakin, S. Sarvotham, and R. G. Baraniuk, "Distributed compressive sensing," arXiv Preprint arXiv:0901. 3403 2005 [Online]. Available: http://arxiv.org/abs/0901.3403
22. 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, 2006.
23. R. Gribonval, H. Rauhut, K. Schnass, and P. Vandergheynst, "Atoms of all channels, unite! average case analysis of multi-channel sparse recovery using greedy algorithms," J. Fourier Anal. Appl., vol. 14, no. 5-6, pp. 655-687, 2008.
24. Y. C. Eldar and H. Rauhut, "Average case analysis of multichannel sparse recovery using convex relaxation," IEEE Trans. Inf. Theory, vol. 56, no. 1, pp. 505-519, 2010.
25. A. Scaglione and A. Vosoughi, "Turbo estimation of channel and symbols in precoded MIMO systems," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 2004, vol. 4, pp. iv413-iv416.
26. D. Tse and P.Viswanath, Fundamentals ofWireless Communication. Cambridge, U.K.: Cambridge Univ. Press, 2005.
27. L. Bottou and N. Murata, "Stochastic approximations and efficient learning," in The Handbook of Brain Theory and Neural Networks, 2nd ed. Cambridge, MA, USA: MIT Press, 2002.
28. C. Berger, S. Zhou, J. Preisig, and P. Willett, "Sparse channel estimation for multicarrier underwater acoustic communication: From subspace methods to compressed sensing," IEEE Trans. Signal Process., vol. 58, no. 3, pp. 1708-1721, 2010.
29. E. Candes and T. Tao, "Decoding by linear programming," IEEE Trans. Inf. Theory, vol. 51, no. 12, pp. 4203-4215, 2005.
30. 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, 2007.
31. D. Needell and J. A. Tropp, "COSAMP: Iterative signal recovery from incomplete and inaccurate samples," Appl. Comput. Harmon. Anal., vol. 26, no. 3, pp. 301-321, 2009.
32. W. Dai and O. Milenkovic, "Subspace pursuit for compressive sensing signal reconstruction," IEEE Trans. Inf. Theory, vol. 55, no. 5, pp. 2230-2249, 2009.
33. 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, 2011.
34. J. A. Tropp, A. C. Gilbert, and M. J. Strauss, "Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit," Signal Process., vol. 86, no. 3, pp. 572-588, 2006.
35. Y. C. Eldar and M. Mishali, "Robust recovery of signals from a structured union of subspaces," IEEE Trans. Inf. Theory, vol. 55, no. 11, pp. 5302-5316, 2009.
36. J. A. Tropp, "Algorithms for simultaneous sparse approximation. Part II: Convex relaxation," Signal Process., vol. 86, no. 3, pp. 589-602, 2006.
37. J. Liang, Y. Liu, W. Zhang, Y. Xu, X. Gan, and X. Wang, "Joint compressive sensing in wideband cognitive networks," in Proc. IEEEWireless Commun. Netw. Conf. (WCNC), 2010, pp. 1-5.
38. R. G. Baraniuk, V. Cevher,M. F. Duarte, and C. Hegde, "Model-based compressive sensing," IEEE Trans. Inf. Theory, vol. 56, no. 4, pp. 1982-2001, 2010.
39. E. J. Candès and M. B. Wakin, "An introduction to compressive sampling," IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21-30, 2008.
40. X. Rao and V. Lau, "Distributed compressive CSIT estimation and feedback for FDD multi-user massiveMIMO systems," arXiv Preprint arXiv:1405.2786 2014 [Online]. Available: http://arxiv.org/abs/1405. 2786
41. G. Sideratos and N. D. Hatziargyriou, "An advanced statistical method for wind power forecasting," IEEE Trans. Power Syst., vol. 22, no. 1, pp. 258-265, 2007.
42. J. Flum and M. Grohe, Parameterized Complexity Theory. New York, NY, USA: Springer, 2006, vol. 3.
43. "Universal Mobile Telecommunications System (UMTS); Spacial channel model for multiple input multiple output (MIMO) simulations," Tech. Rep. G. T. 25.996, 3GPP ETSI Release 9, 2010 [Online]. Available: http://www.3gpp.org/ DynaReport/25996.htm
44. M. Klessling, J. Speidel, and Y. Chen, "MIMO channel estimation in correlated fading environments," in Proc. IEEE Veh. Technol. Conf. (VTC), 2003, vol. 2, pp. 1187-1191.