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LIU Yi, MEI Wen-bo, DU Hui-qian, WANG Hong-yu. Sparse channel estimation for MIMO-OFDM systems using distributed compressed sensing[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2016, 25(4): 540-546. doi: 10.15918/j.jbit1004-0579.201625.0413
Citation: LIU Yi, MEI Wen-bo, DU Hui-qian, WANG Hong-yu. Sparse channel estimation for MIMO-OFDM systems using distributed compressed sensing[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2016, 25(4): 540-546.doi:10.15918/j.jbit1004-0579.201625.0413

Sparse channel estimation for MIMO-OFDM systems using distributed compressed sensing

doi:10.15918/j.jbit1004-0579.201625.0413
  • Received Date:2015-04-06
  • A sparse channel estimation method is proposed for doubly selective channels in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Based on the basis expansion model (BEM) of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing (DCS) problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing (CS) theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.
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  • [1]
    Ozdemir M K, Arslan H. Channel estimation for wireless OFDM systems [J]. IEEE Communications Surveys and Tutorials, 2007, 9(2): 18-48.
    [2]
    Ye L, Winters J H, Sollenberger N R. MIMO-OFDM for wireless communications: signal detection with enhanced channel estimation [J]. IEEE Transactions on Communications, 2002, 50(9): 1471-1477.
    [3]
    Donoho D L. Compressed sensing [J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
    [4]
    Candès E J, Wakin M B. An introduction to compressive sampling [J]. IEEE Signal Processing Magazine, 2008, 25(2): 21-30.
    [5]
    Bajwa W U, Haupt J, Sayeed A M, et al. Compressed channel sensing: a new approach to estimating sparse multipath channels [J]. Proceedings of the IEEE, 2010, 98(6): 1058-1076.
    [6]
    Tauböck G, Hlawatsch F, Eiwen D, et al. Compressive estimation of doubly selective channels in multicarrier systems: leakage effects and sparsity-enhancing processing [J]. IEEE Selected Topics in Signal Processing, 2010, 4(2): 255-271.
    [7]
    Bajwa W U, Sayeed A, Nowak R. Compressed sensing of wireless channels in time, frequency, and space[C]//Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA, 2008.
    [8]
    Meng J, Yin W, Li Y, et al. Compressive sensing based high-resolution channel estimation for OFDM system [J]. IEEE Journal of Selected Topics in Signal Processing, 2012, 6(1): 15-25.
    [9]
    Huang J, Zhou S, Huang J, et al. Progressive inter-carrier interference equalization for OFDM transmission over time-varying underwater acoustic channels [J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(8): 1524-1536.
    [10]
    Berger C R, Wang Z, Huang J, et al. Application of compressive sensing to sparse channel estimation [J]. IEEE Communications Magazine, 2010, 48(11): 164-174.
    [11]
    Duarte M F, Sarvotham Shriram, Baron D. Distributed compressed sensing of jointly sparse signals[C]//Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA, 2005.
    [12]
    Duarte M F, Eldar Y C. Structured compressed sensing: from theory to applications [J]. IEEE Transactions on Signal Processing, 2011, 59(9): 4053-4085.
    [13]
    Eldar Y C, Mishalii M. Robust recovery of signals from a structured union of subspaces [J]. IEEE Transactions on Information Theory, 2009, 55(11): 5302-5316.
    [14]
    Eldar Y C, Kuppinger P, Bolcskei H. Block-sparse signals: uncertainty relations and efficient recovery [J]. IEEE Transactions on Signal Processing, 2010, 58(6): 3042-3054.
    [15]
    Donoho D L, Tsaig Y. Fast solution of l1-norm minimization problems when the solution may be sparse [J]. IEEE Transactions on Information Theory, 2008, 54(11): 4789-4812.
    [16]
    Zhang T. Sparse recovery with orthogonal matching pursuit under RIP [J]. IEEE Transactions on Information Theory, 2011, 57(9): 6215-6221.
    [17]
    Candès E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information [J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
    [18]
    Liu Y, Mei M, Du H. Model-based compressive channel estimation over rapidly time-varying channels in OFDM systems [J]. IEICE Transactions on Communications, 2014, E97-B(8): 1709-1716.
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