Back to Search
Start Over
Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach.
- Source :
-
IEEE Transactions on Signal Processing . Oct2015, Vol. 63 Issue 20, p5369-5382. 14p. - Publication Year :
- 2015
-
Abstract
- 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., the N_tN_r 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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 1053587X
- Volume :
- 63
- Issue :
- 20
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 109456235
- Full Text :
- https://doi.org/10.1109/TSP.2015.2451071