Low-complexity banded equalizers for OFDM systems in Doppler spread channels
EURASIP Journal on Applied Signal Processing
Efficient sequence detection of multicarrier transmissions over doubly dispersive channels
EURASIP Journal on Applied Signal Processing
Intercarrier interference in MIMO OFDM
IEEE Transactions on Signal Processing
Low-complexity equalization of OFDM in doubly selective channels
IEEE Transactions on Signal Processing
Markov chain Monte Carlo algorithms for CDMA and MIMO communication systems
IEEE Transactions on Signal Processing
Equalization for OFDM over doubly selective channels
IEEE Transactions on Signal Processing
Iterative interference cancellation and channel estimation for mobile OFDM
IEEE Transactions on Wireless Communications
Iterative Channel Estimation and Decoding of Turbo Coded SFBC-OFDM Systems
IEEE Transactions on Wireless Communications
IEEE Communications Magazine
IEEE Transactions on Signal Processing
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In orthogonal frequency-division multiplexing (OFDM) systems operating over rapidly time-varying channels, the orthogonality between subcarriers is destroyed leading to inter-carrier interference (ICI) and resulting in an irreducible error floor. In this paper, a new and low-complexity maximum a posteriori probability (MAP) detection algorithm is proposed for OFDM systems operating over rapidly time-varying multipath channels. The detection algorithm exploits the banded structure of the frequency-domain channel matrix whose bandwidth is a parameter to be adjusted according to the speed of the mobile terminal. Based on this assumption, the received signal vector is decomposed into reduced dimensional sub-observations in such a way that all components of the observation vector contributing to the symbol to be detected are included in the decomposed observation model. The data symbols are then detected by the MAP algorithm by means of a Markov chain Monte Carlo (MCMC) technique in an optimal and computationally efficient way. Computational complexity investigation as well as simulation results indicate that this algorithm has significant performance and complexity advantages over existing suboptimal detection and equalization algorithms proposed earlier in the literature.