Joint CFO and sparse channel estimation for MIMO-OFDM systems via the SAGE algorithm

In this paper, we investigate the problem of joint carrier frequency offset (CFO) and sparse channel estimation in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The conventional channel estimation methods using least-squares (LS) fail to exploit the sparsity of wireless channel, which results in a poor performance and a long training sequence in a sparse scenario. The proposed algorithm uses subspace pursuit instead. Furthermore, an iterative space-alternation generalized expectation-maximization (SAGE) estimator is proposed to provide a solution to the complex multi-dimensional extreme-value search problem. We also derive the Cramér-Rao lower bound (CRLB) for multiple parameters estimation. Simulation results show that the proposed algorithm achieves a better performance compared with the conventional iterative joint algorithms.

[1]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[2]  Robert L. Frank,et al.  Polyphase codes with good nonperiodic correlation properties , 1963, IEEE Trans. Inf. Theory.

[3]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[4]  Olgica Milenkovic,et al.  Subspace Pursuit for Compressive Sensing Signal Reconstruction , 2008, IEEE Transactions on Information Theory.

[5]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[6]  Mehrdad Ardebilipour,et al.  Joint maximum-likelihood frequency offset and channel estimation for multiple-input multiple-output-orthogonal frequency-division multiplexing systems , 2008, IET Commun..

[7]  Petre Stoica,et al.  On parameter estimation of MIMO flat-fading channels with frequency offsets , 2003, IEEE Trans. Signal Process..

[8]  Hlaing Minn,et al.  Optimal training signals for MIMO OFDM channel estimation , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[9]  David C. Chu,et al.  Polyphase codes with good periodic correlation properties (Corresp.) , 1972, IEEE Trans. Inf. Theory.

[10]  Petre Stoica,et al.  Training sequence design for frequency offset and frequency-selective channel estimation , 2003, IEEE Trans. Commun..

[11]  Geoffrey Ye Li,et al.  Broadband MIMO-OFDM wireless communications , 2004, Proceedings of the IEEE.

[12]  Tung-Sang Ng,et al.  Correlation-based frequency offset estimation in MIMO system , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[13]  Shihua Zhu,et al.  Joint CFO and Channel Estimation for Asynchronous Cooperative Communication Systems , 2012, IEEE Signal Processing Letters.

[14]  Stefania Sesia,et al.  LTE - The UMTS Long Term Evolution, Second Edition , 2011 .

[15]  Shengli Zhou,et al.  Application of compressive sensing to sparse channel estimation , 2010, IEEE Communications Magazine.

[16]  Tung-Sang Ng,et al.  Joint CFO and Channel Estimation for Multiuser MIMO-OFDM Systems With Optimal Training Sequences , 2008, IEEE Transactions on Signal Processing.

[17]  Alfred O. Hero,et al.  Space-alternating generalized expectation-maximization algorithm , 1994, IEEE Trans. Signal Process..

[18]  Ying-Chang Liang,et al.  Joint channel and frequency offset estimation in distributed MIMO flat-fading channels , 2008, IEEE Transactions on Wireless Communications.

[19]  C.-C. Jay Kuo,et al.  Synchronization Techniques for Orthogonal Frequency Division Multiple Access (OFDMA): A Tutorial Review , 2007, Proceedings of the IEEE.