Adaptive sparse channel estimation for time-variant MIMO-OFDM systems

Accurate channel state information (CSI) is required for coherent detection in time-variant multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) modulation. One of low-complexity and stable adaptive channel estimation (ACE) approaches is the normalized least mean square (NLMS)-based ACE. However, it cannot exploit the inherent sparsity of MIMO channel which is characterized by a few dominant channel taps. In this paper, we propose two adaptive sparse channel estimation (ASCE) methods to take advantage of such sparse structure information for time-variant MIMO-OFDM systems. Unlike traditional NLMS-based method, two proposed methods are implemented by introducing sparse penalties to the cost function of NLMS algorithm. Computer simulations confirm obvious performance advantages of the proposed ASCEs over the traditional ACE.

[1]  Yuantao Gu,et al.  $l_{0}$ Norm Constraint LMS Algorithm for Sparse System Identification , 2009, IEEE Signal Processing Letters.

[2]  Alfred O. Hero,et al.  Sparse LMS for system identification , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Jiang Yue,et al.  Channel estimation and data detection for MIMO-OFDM systems , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[4]  Fumiyuki Adachi,et al.  Improved adaptive sparse channel estimation based on the least mean square algorithm , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[5]  S. Thomas Alexander,et al.  Adaptive Signal Processing , 1986, Texts and Monographs in Computer Science.

[6]  Xuefeng Yin,et al.  Cluster Characteristics in a MIMO Indoor Propagation Environment , 2007, IEEE Transactions on Wireless Communications.

[7]  Zhu Han,et al.  A MIMO-OFDM channel estimation approach using time of arrivals , 2005, IEEE Transactions on Wireless Communications.

[8]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[9]  Yan Zhou,et al.  Experimental Study of MIMO Channel Statistics and Capacity via Virtual Channel Representation , 2006 .

[10]  Fumiyuki Adachi,et al.  New direction of broadband wireless technology , 2007, Wirel. Commun. Mob. Comput..

[11]  Fumiyuki Adachi,et al.  Broadband CDMA techniques , 2005, IEEE Wireless Communications.

[12]  Sergiy A. Vorobyov,et al.  Sparse channel estimation with lp-norm and reweighted l1-norm penalized least mean squares , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  Dipankar Raychaudhuri,et al.  Frontiers of Wireless and Mobile Communications , 2012, Proceedings of the IEEE.

[14]  P. Vainikainen,et al.  Measurement of Large-Scale Cluster Power Characteristics for Geometric Channel Models , 2007, IEEE Transactions on Antennas and Propagation.

[15]  Alex B. Gershman,et al.  Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals , 2006, IEEE Transactions on Signal Processing.

[16]  Michael Unser,et al.  Compressibility of Deterministic and Random Infinite Sequences , 2011, IEEE Transactions on Signal Processing.

[17]  E. Candès.  The restricted isometry property and its implications for compressed sensing , 2008 .

[18]  Zhi Zhang,et al.  A Novel Sparse Channel Estimation Method for Multipath MIMO-OFDM Systems , 2011, 2011 IEEE Vehicular Technology Conference (VTC Fall).

[19]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[20]  Holger Rauhut,et al.  Compressive Estimation of Doubly Selective Channels in Multicarrier Systems: Leakage Effects and Sparsity-Enhancing Processing , 2009, IEEE Journal of Selected Topics in Signal Processing.

[21]  Jitendra K. Tugnait,et al.  On Superimposed Training for MIMO Channel Estimation and Symbol Detection , 2007, IEEE Transactions on Signal Processing.

[22]  Robert D. Nowak,et al.  Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels , 2010, Proceedings of the IEEE.

[23]  Ying-Chang Liang,et al.  A joint channel estimation and data detection receiver for multiuser MIMO IFDMA systems , 2009, IEEE Transactions on Communications.

[24]  Chong-Yung Chi,et al.  Training Sequence Design for Discriminatory Channel Estimation in Wireless MIMO Systems , 2010, IEEE Transactions on Signal Processing.