期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 62, 期 14, 页码 3591-3603出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2014.2329272
关键词
OFDM; expectation maximization; sparse Bayesian learning; channel estimation; a-sparse; Kalman filtering and smoothing
资金
- Indo-US Virtual Institute for Mathematical and Statistical Sciences
- Tata Consultancy Services Research Scholar Program
- Indo-UK Advanced Technology Centre
- NSF [CCF-1115645]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1115645] Funding Source: National Science Foundation
It is well known that the impulse response of a wide-band wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. In this paper, we consider the estimation of the unknown channel coefficients and its support in OFDM systems using a sparse Bayesian learning (SBL) framework for exact inference. In a quasi-static, block-fading scenario, we employ the SBL algorithm for channel estimation and propose a joint SBL (J-SBL) and a low-complexity recursive J-SBL algorithm for joint channel estimation and data detection. In a time-varying scenario, we use a first-order autoregressive model for the wireless channel and propose a novel, recursive, low-complexity Kalman filtering-based SBL (KSBL) algorithm for channel estimation. We generalize the KSBL algorithm to obtain the recursive joint KSBL algorithm that performs joint channel estimation and data detection. Our algorithms can efficiently recover a group of approximately sparse vectors even when the measurement matrix is partially unknown due to the presence of unknown data symbols. Moreover, the algorithms can fully exploit the correlation structure in the multiple measurements. Monte Carlo simulations illustrate the efficacy of the proposed techniques in terms of the mean-square error and bit error rate performance.
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