Expectation propagation for signal detection in flat-fading channels
In this paper, we propose a new Bayesian receiver for signal detection in flat- fading channels. First, the detection prob- lem is formulated as an inference problem in a hybrid dynamic system. Then, an ex- pectation propagation algorithm is proposed to address the inference problem. Our sim- ulations demonstrate that the new receiver achieves accurate detection without the aid of any training symbols or decision feedbacks. Due to the increased use of various wireless ser- vices in our daily life, adaptive signal detection in flat Rayleigh fading channels has become an impor- tant research topic. Different approaches have been proposed to address this problem (5, 2, 1, 61. The most accurate approach so far employs sequential Monte Carlo filters and smoothers 11, 61. In this ap proach, signal detection and decoding is treated as an estimation problem in a hybrid dynamic system that has both continuous channel coefficients and discrete uncoded information bits. Exact estimation is intractable, so sequential Monte Carlo is used to get an approximation. This approach gives excel- lent performance in simulations (l, 61. However, the computational cost is prohibitive in practice. In this paper, we develop an efficient expectation propagation (EP) algorithm for hybrid dynamic sys- tems and apply it to adaptive detection in flat-fading channels. EP (4) is a powerful extension of belief propagation (BP). BP has been used in digital com- munications, such as Turbo decoding (3). However, BP can handle only discrete distributions or con- tinuous distributions in the exponential family. In contrast, EP not only iteratively propagates the in- formation in the dynamic system, but also projects exact messages into the exponential family. Con- sequently, EP can efficiently approximate Bayesian