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In orthogonal frequency-division multiplexing (OFDM) systems operating overrapidly time-varying channels, the orthogonality between subcarriers isdestroyed leading to inter-carrier interference (ICI) and resulting in anirreducible error floor. In this paper, a new and low-complexity maximum {\em aposteriori} probability (MAP) detection algorithm is proposed for OFDM systemsoperating over rapidly time-varying multipath channels. The detection algorithmexploits the banded structure of the frequency-domain channel matrix whosebandwidth is a parameter to be adjusted according to the speed of the mobileterminal. Based on this assumption, the received signal vector is decomposedinto reduced dimensional sub-observations in such a way that all components ofthe observation vector contributing to the symbol to be detected are includedin the decomposed observation model. The data symbols are then detected by theMAP algorithm by means of a Markov chain Monte Carlo (MCMC) technique in anoptimal and computationally efficient way. Computational complexityinvestigation as well as simulation results indicate that this algorithm hassignificant performance and complexity advantages over existing suboptimaldetection and equalization algorithms proposed earlier in the literature.

A Gibbs Sampling Based MAP Detection Algorithm for OFDM Over Rapidly Varying Mobile Radio Channels