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Novel schemes of CQI Feedback Compression based on Compressive Sensing for Adaptive OFDM Transmission

  • Li, Yongjie (College of Telecommunications & Information Engineering, Nanjing University of Posts & Telecommunications Nanjing) ;
  • Song, Rongfang (National Mobile Communications Research Laboratory, Southeast University)
  • Received : 2011.01.06
  • Accepted : 2011.03.28
  • Published : 2011.04.29

Abstract

In multi-user wireless communication systems, adaptive modulation and scheduling are promising techniques for increasing the system throughput. However, a mass of wireless recourse will be occupied and spectrum efficiency will be decreased to feedback channel quality indication (CQI) of all users in every subcarrier or chunk for adaptive orthogonal frequency division multiplexing (OFDM) systems. Thus numerous limited feedback schemes are proposed to reduce the system overhead. The recently proposed compressive sensing (CS) theory provides a new framework to jointly measure and compress signals that allows less sampling and storage resources than traditional approaches based on Nyquist sampling. In this paper, we proposed two novel CQI feedback schemes based on general CS and subspace CS, respectively, both of which could be used in a wireless OFDM system. The feedback rate with subspace CS is greatly decreased by exploiting the subspace information of the underlying signal. Simulation results show the effectiveness of the proposed methods, with the same feedback rate, the throughputs with subspace CS outperform the discrete cosine transform (DCT) based method which is usually employed, and the throughputs with general CS outperform DCT when the feedback rate is larger than 0.13 bits/subcarrier.

Keywords

References

  1. Satoru Iijima, Rui Zhou and Iwao Sasase, "Reduction of feedback overload by exploiting adaptive channel in OFDMA systems," IEEE Pacific Rim Conference Rim Conference on Communications, Computers and Signal Processing (PACRIM2007), pp.153-156, Victoria, Canada, Aug. 2007.
  2. Wei-Linag Li, Ying Jun Zhang, Anthony Man-Cho So and Moe Z. Win, "Slow Adaptive OFDMA Systems Through Chance Constrained Programming," IEEE Trans. Signal Process., vol. 58, no. 7, pp. 3858-3869, Jul. 2010. https://doi.org/10.1109/TSP.2010.2046434
  3. Wei-Linag Li, Ying Jun Zhang and Moe Z. Win, "Slow adaptive OFDMA via stochastic programming," in Proc. IEEE Int. Conf. on Commun., Dresden, Germany, pp. 1-6, Jun. 2009.
  4. A. Forenza, M. Airy, M. Kountouris, R. Heath, D. Gesbert and S. Shakkottai, "Performance of the MIMO Downlink Channel with Multi-Mode Adaptation and Scheduling," in Proc. 6th IEEE Workshop on Signal Proc. Advances in Wireless Commun. (SPAWC 2005), pp. 695 - 699, New York, USA, Jun. 2005.
  5. James Gross, Hans-florian Geerdes, Holger Karl and Adam Wolisz, "Performance analysis of dynamic OFDMA systems with inband signaling," IEEE J. Sel. Areas Commun., vol. 24, no. 3, pp. 427-436, Mar. 2006. https://doi.org/10.1109/JSAC.2005.862386
  6. R. Knopp and P. A. Humblet, "Information capacity and power control in single-cell multiuser communications," in Proc. IEEE International Conference on Communications, vol. 1, pp. 331-335, 1995.
  7. T. Eriksson, T. Ottosson, "Compression of feedback for adaptive transmission and scheduling," Proceedings of the IEEE, vol. 95, no. 12, pp. 2314-2321, Dec. 2007. https://doi.org/10.1109/JPROC.2007.907131
  8. T. Eriksson, T. Ottosson, "Compression of feedback in adaptive OFDM-based systems using scheduling," IEEE Communication Letters, vol. 11, no. 11, pp. 859-861, Nov. 2007. https://doi.org/10.1109/LCOMM.2007.070543
  9. Erlin ZENG, shihua ZHU and Ming XU, "CQI feedback overhead reduction for multicarrier MIMO transmission," IEICE trans. communication, vol. E91-B, no. 7, pp. 2310-2320, July. 2008 https://doi.org/10.1093/ietcom/e91-b.7.2310
  10. E. J. 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, Feb. 2006. https://doi.org/10.1109/TIT.2005.862083
  11. D. L. Donoho, "Compressed sensing," IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006. https://doi.org/10.1109/TIT.2006.871582
  12. P. T. Boufounos and R. G. Baraniuk, "1-Bit compressive sensing," in Proc. 42nd Annual Conference on Information Science and System, pp. 16-21, Mar. 2008.
  13. D. L. Donoho, "For most large underdetermined systems of equations, the minimal $l_{1}$-norm near- solution approximates the sparsest near-solution," Comm. Pure and Applied Math., vol. 59, no. 7, pp. 907-934, 2006. https://doi.org/10.1002/cpa.20131
  14. E. J. Candes and M. B. Wakin, "An introduction to compressive sampling," IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21-30, Mar. 2008. https://doi.org/10.1109/MSP.2007.914731
  15. Z. Wang, G. R. Arce, and B. M. Sadler, "Subspace compressive detection for sparse signals," in Proc. ICASSP, Las Vegas, NV, Mar. 2008.
  16. Zhongmin Wang, "New Sampling and Detection Approaches for Compressed Sensing and their application to Ultra Wideband Communications," Dissertation for the Doctoral Degree, University of Delaware, 2010.
  17. A. Goldsmith, Wireless Communication, Chapter 6, Cambridge University Press, 2005.
  18. J. L. Vicario, R. Bosisio. U. Spagnolini and C. Anton-Haro, "A throughput analysis for opportunistic beamforming with quantized feedback," in Proc. IEEE PIMRC, pp. 1 - 5, Sep. 2006.
  19. A. C. Gilbert, S. Muthukrishnan and M. J. Strauss, "Approximation of functions over redundant dictionaries using coherence," in The 14th Annual ACM-SIAM Symposium on Discrete Algorithms, Jan. 2003.
  20. D. L. Donoho and X. Huo, "Uncertainty principles and ideal atomic decomposition," IEEE Trans. Inform. Theory, vol. 47, pp. 2845-2862, Nov. 2001. https://doi.org/10.1109/18.959265
  21. M. Elad, "Optimized projections for compressed sensing," IEEE Trans. Signal Process., vol. 55, no. 12, pp. 5695-5702, Dec. 2007. https://doi.org/10.1109/TSP.2007.900760

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