On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Sequential Monte Carlo sampling detector for Rayleigh fast-fading channels
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01
Adaptive joint detection and decoding in flat-fading channels via mixture Kalman filtering
IEEE Transactions on Information Theory
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Detection of data transmitted over a Rayleigh fading channel, where the channel is unknown, has been a problem of interest for many researchers. In this paper, we present a new algorithm for joint detection and channel estimation for Rayleigh fading channels. Our algorithm combines Monte Carlo sampling with classical recursive identification methods. The channel is modeled as an autoregressive process, which allows for representation of the communication system by a dynamic state space model. A more accurate modeling of the channel, especially in fast fading along with exploitation of time diversity in the received signal, is also considered. Simulation results illustrating the effectiveness of this algorithm are presented.