Adaptive joint detection and decoding in flat-fading channels via mixture Kalman filtering

A novel adaptive Bayesian receiver for signal detection in flat-fading channels is developed based on the sequential Monte Carlo methodology. The basic idea is to treat the transmitted signals as missing data and to sequentially impute multiple copies of them based on the observed signals. The imputed signal sequences, together with their importance weights, provide a way to approximate the Bayesian estimate of the transmitted signals and the channel states. It is shown through simulations that the proposed sequential Monte Carlo receivers achieve near-bound performance in fading channels without the aid of any training/pilot symbols or decision feedback. Moreover, the proposed receiver structure exhibits massive parallelism and is ideally suited for high-speed parallel implementation using the VLSI systolic array technology.