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

• Published in: IEEE transactions on signal processing Vol. 63; no. 20; pp. 5369 - 5382
• Main Authors: Prasad, Ranjitha, Murthy, Chandra R., Rao, Bhaskar D.
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.10.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Channel estimation; cluster sparsity; joint channel estimation and data detection; joint sparsity; Joints; Mean square errors; Monte Carlo simulation; multiple measurement vectors; OFDM; Receiving antennas; Signal processing algorithms; Sparse Bayesian learning; Transmitting antennas; Wireless communication
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2015.2451071

The impulse response of wireless channels between the Nt transmit and Nr receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., the NtNr 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.