Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

被引:99
作者
Prasad, Ranjitha [1 ]
Murthy, Chandra R. [2 ]
Rao, Bhaskar D. [3 ]
机构
[1] Indian Inst Sci IISc, Dept Elect & Commun Engn ECE, Bangalore, Karnataka, India
[2] IISc, Dept Elect & Commun Engn ECE, Bangalore, Karnataka, India
[3] San Diego State Univ, Dept Elect & Comp Engn, San Diego, CA 92037 USA
关键词
Sparse Bayesian learning; joint channel estimation and data detection; joint sparsity; cluster sparsity; multiple measurement vectors; APPROXIMATION; ALGORITHMS; MODELS;
D O I
10.1109/TSP.2015.2451071
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The impulse response of wireless channels between the N-t transmit and N-r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., NtNt the channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.
引用
收藏
页码:5369 / 5382
页数:14
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