Expectation Propagation-Based Sampling Decoding: Enhancement and Optimization

Abstract

In this paper, the paradigm of expectation propagation (EP) algorithm in large-scale MIMO detection is extended by the sampling decoding in an Markov chain Monte Carlo way to boost the approximation of the target posterior distribution. The proposed EP-based sampling decoding scheme not only theoretically addresses the inherent convergence problem of EP, but also is able to achieve the near-optimal decoding performance with the increment of Markov moves. Specifically, the EP-based independent Metropolis-Hastings (MH) is proposed to guarantee the exponential convergence to the target posterior distribution, thus bridging the EP detector and the sampling decoding as a whole. Meanwhile, the output yielded by the EP detector also provides a good initial setup for the sampling decoding, which results in a better convergence performance in the approximation. To further improve the convergence performance and the decoding efficiency, the EP-based Gibbs sampling is given, where the choice of the standard deviation of the discrete Gaussian distribution in the Markov mixing is also studied for a better decoding performance. Moreover, we extend the proposed EP-based Gibbs sampling decoding to the soft-output decoding in MIMO bit-interleaved coded modulation (BICM) systems, which enjoys a flexible decoding trade-off between performance and complexity by the number of Markov moves.