Riccati equation and EM algorithm convergence for inertial navigation alignment
IEEE Transactions on Signal Processing
Recursive channel estimation algorithms for iterative receiver in MIMO-OFDM systems
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Frequency domain estimation of time varying channels in OFDMA systems: an EM approach
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
A forward-backward Kalman filter-based STBC MIMO OFDM receiver
EURASIP Journal on Advances in Signal Processing
Low complexity variational bayes iterative receiver for MIMO-OFDM systems
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Cyclic prefix based enhanced data recovery in OFDM
IEEE Transactions on Signal Processing
A model reduction approach for OFDM channel estimation under high mobility conditions
IEEE Transactions on Signal Processing
Data-aided SNR estimation in time-variant Rayleigh fading channels
IEEE Transactions on Signal Processing
Cross-ambiguity function domain multipath channel parameter estimation
Digital Signal Processing
Expectation maximization approach to data-based fault diagnostics
Information Sciences: an International Journal
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Orthogonal frequency division multiplexing (OFDM) combines the advantages of high achievable rates and relatively easy implementation. However, for proper recovery of the input, the OFDM receiver needs accurate channel information. In this paper, we propose an expectation-maximization algorithm for joint channel and data recovery in fast fading environments. The algorithm makes a collective use of the data and channel constraints inherent in the communication problem. This comes in contrast to other works which have employed these constraints selectively. The data constraints include pilots, the cyclic prefix, and the finite alphabet restriction, while the channel constraints include sparsity, finite delay spread, and the statistical properties of the channel (frequency and time correlation). The algorithm boils down to a forward-backward Kalman filter. We also suggest a suboptimal modification that is able to track the channel and recover the data with no latency. Simulations show the favorable behavior of both algorithms compared to other channel estimation techniques.