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

Abstract

A novel adaptive Bayesian receiver for signal detection and decoding in fading channels with known channel statistics is developed; it is based on the sequential Monte Carlo methodology that has emerged in the field of statistics. The basic idea is to treat the transmitted signals as "missing data" and to sequentially impute multiple samples 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. Adaptive receiver algorithms for both uncoded and convolutionally coded systems are developed. The proposed techniques can easily handle the non-Gaussian ambient channel noise. It is shown through simulations that the proposed sequential Monte Carlo receivers achieve near-bound performance in fading channels for both uncoded and coded systems, without the use 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 very large scale integration (VLSI) systolic array technology.