DOI QR코드

DOI QR Code

Low Dimensional Multiuser Detection Exploiting Low User Activity

  • 투고 : 2012.05.07
  • 심사 : 2013.03.11
  • 발행 : 2013.06.30

초록

In this paper, we propose new multiuser detectors (MUDs) based on compressed sensing approaches for the large-scale multiple antenna systems equipped with dozens of low-power antennas. We consider the scenarios where the number of receiver antennas is smaller than the total number of users, but the number of active users is relatively small. This prior information motivates sparsity-embracing MUDs such as sparsity-embracing linear/nonlinear MUDs where the detection of active users and their symbol detection are employed. In addition, sparsity-embracing MUDs with maximum a posteriori probability criterion (MAP-MUDs) are presented. They jointly detect active users and their symbols by exploiting the probability of user activity, and it can be solved efficiently by introducing convex relaxing senses. Furthermore, it is shown that sparsity-embracing MUDs exploiting common users' activity across multiple symbols, i.e., frame-by-frame, can be considered to improve performance. Also, in multiple multiple-input and multiple-output networks with aggressive frequency reuse, we propose the interference cancellation strategy for the proposed sparsity-embracing MUDs. That first cancels out the interference induced by adjacent networks and then recovers the desired users' information by exploiting the low user activity. In simulation studies for binary phase shift keying modulation, numerical evidences establish the effectiveness of our proposed MUDs exploiting low user activity, as compared with the conventional MUD.

키워드

과제정보

연구 과제 주관 기관 : Korea Communications Agency (KCA)

참고문헌

  1. H. Zhu and G. B. Giannakis, "Exploiting sparse user activity in multiuser detection," IEEE Trans. Commun. vol. 59, No. 2, pp. 454-465, Feb. 2011. https://doi.org/10.1109/TCOMM.2011.121410.090570
  2. A. Pitarokoilis, S. K. Mohammed, and E. G. Larsson, "On the optimality of single-carrier transmission in large-scale antenna systems," IEEE Wireless Commun. Lett., vol. 1, no. 4, pp. 276-279, Aug. 2012. https://doi.org/10.1109/WCL.2012.041612.120046
  3. N. Srinidhi, T. Datta, A. Chockalingam, and B. S. Rajan, "Layered tabu search algorithm for large-MIMO detection and a lower bound on ML performance," IEEE Trans. Commun., vol. 59, no. 11, pp. 2955-2963, Nov. 2011. https://doi.org/10.1109/TCOMM.2011.070511.110058
  4. T. L. Marzetta, "Noncooperative cellular wireless with unlimited number of base station antennas," IEEE Trans. Wireless Commun., vol. 9, no. 11, pp. 3590-3600, Nov. 2010. https://doi.org/10.1109/TWC.2010.092810.091092
  5. P. Li and R. D. Murch, "Multiple output selection-LAS algorithm in large MIMO systems," IEEE Commun. Lett., vol. 14, no. 5, pp. 399-401, May 2010. https://doi.org/10.1109/LCOMM.2010.05.100092
  6. E. Berg and M. Friedlander, "Theoretical and empirical results for recovery from multiple measurements," IEEE Trans. Info. Theory, vol. 56, no. 5, May 2010.
  7. M. Mishali and Y. C. Eldar, "Reduce and boost: Recovering arbitrary sets of jointly sparse vectors," IEEE Trans. Signal Proc., vol. 56, no. 10, pp. 4692-4702, Oct. 2008. https://doi.org/10.1109/TSP.2008.927802
  8. J. Chen and X. Huo, "Theoretical results on sparse representations of multiple measurement vectors," IEEE Trans. Signal Proc., vol. 54, no. 12, pp. 4634-4643, Dec. 2006. https://doi.org/10.1109/TSP.2006.881263
  9. S. Cotter, B. Rao, K. Engan, and K. Kreutz-Delgado, "Sparse solutions to linear inverse problems with multiple measurement vectors," IEEE Trans. Signal Proc., vol 53, no. 7, pp. 2477-2488, July 2005. https://doi.org/10.1109/TSP.2005.849172
  10. W. U. Bajwa, G. Raz, and R. Nowak, "Toeplitz compressed sensing matrices with applications to sparse channel estimation," IEEE Trans. Info. Theory, vol. 56, no. 11, pp. 5862-5875, Nov. 2010. https://doi.org/10.1109/TIT.2010.2070191
  11. J. Bazerque and G. Giannakis, "Distributed spectrum sensing for cognitive radio networks by exploiting sparsity," IEEE Trans. Signal Proc., vol. 58, no. 3, pp. 1847-1862, Mar. 2010. https://doi.org/10.1109/TSP.2009.2038417
  12. M. Mishali and Y. C. Eldar, "From theory to practice: Sub-Nyquist sampling of sparse wideband analog signals," IEEE J. Select. Top. Signal Proc., vol. 4, no. 2, pp. 375-391, Apr. 2010. https://doi.org/10.1109/JSTSP.2010.2042414
  13. M. A. Davenport, P. T. Boufounos, M. B. Wakin, and R. G. Baraniuk, "Signal processing with compressive measurements," IEEE J. Sel. Top. Signals Proc., vol. 4, no. 2, pp. 445-460, Apr. 2010. https://doi.org/10.1109/JSTSP.2009.2039178
  14. E. Biglieri and M. Lops, "Multiuser detection in dynamic environment - part 1: User identification and data detection," IEEE Trans. Info. Theory, vol 53, pp. 3158-3170, Sept. 2007. https://doi.org/10.1109/TIT.2007.903115
  15. J. A. Tropp and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. Info. Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007. https://doi.org/10.1109/TIT.2007.909108
  16. P.W.Wolniansky, G. J. Foschini, G. D. Golden, and R. A. Valenzuela, "VBLAST: An architecture for realizing very high data rates over the richscattering wireless channel," in Proc. URSI ISSSE, Sept. 1998, pp. 295- 300.
  17. S.Mendelson, A. Pajor, and N. T. Jaegermann, "Uniform uncertainty principle for Bernoulli and sub-Gaussian ensembles," Constr. Approx., vol. 28, no. 3, 2006.
  18. R. G. Baraniuk, "Compressive sensing," IEEE Signal Proc. Mag., vol. 24, no. 4, pp.118-120, 124, July 2007.
  19. M. E. Davies and Y. C. Eldar, "Rank awareness in joint sparse recovery," IEEE Trans. Info. Theory, vol. 58, no. 2, Feb. 2012.
  20. Y. C. Eldar and H. Rauhut, "Average case analysis of multichannel sparse recovery using convex relaxation," IEEE Trans. Info. Theory, vol. 56, no. 1, pp. 505-519, Jan. 2010. https://doi.org/10.1109/TIT.2009.2034789
  21. B. O. Lee, O-S. Shin, and K. B. Lee, "Distributed MIMO precoding strategies in a multicell environment," IEEE Commun. Lett., vol. 15, no. 9, Sept. 2011.
  22. W. L. Ho, Q. S. Quek, S. Sun, and R. W. Heath, "Decentralized precoding for multicell MIMO downlink," IEEE Trans. Wireless Commun., vol. 10, no. 6, June 2011.
  23. E. J. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Info. Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006. https://doi.org/10.1109/TIT.2005.862083
  24. E. J. Candes and T. Tao, "Neal optimal signal recovery from random projections: Universal encoding strategies?," IEEE Trans. Info. Theory, vol. 52, no. 12, pp. 5406-5425, Dec. 2006. https://doi.org/10.1109/TIT.2006.885507
  25. Z. B. Haim, Y. C. Eldar, and M. Elas, "Coherence-based performance guarantees for estimating a sparse vector under random noise," IEEE Trans. Signal Process., vol. 58, no. 10, pp. 5030-5043, Oct. 2010. https://doi.org/10.1109/TSP.2010.2052460
  26. D. L. Donoho and M. Elad, "Optimally sparse representation in general (nonorthogonal) dictionaries via L1 minimization," Proc. Nat. Acad. Sci., vol. 100, no. 5, pp. 2197-2202, Mar. 2003. https://doi.org/10.1073/pnas.0437847100
  27. S. Gleichman and Y. C. Eldar, "Blind compressed sensing," IEEE Trans. Info. Theory, vol. 57, no. 10, pp. 6958-6975, Oct. 2011. https://doi.org/10.1109/TIT.2011.2165821