A non-resampling sequential Monte Carlo detector for coded OFDM systems based on periodic termination of differential phase trellis

Abstract

Sequential Monte Carlo (SMC) is a group of methods that use Monte Carlo simulation to solve online estimation problems in dynamic systems. SMC methods are traditionally built on the techniques of sequential importance sampling (SIS) and resampling. In this paper, we apply the SMC methodology to the problem of symbol detection in a differentially encoded orthogonal frequency division multiplexing (OFDM) system over a frequency selective fading channel. We first propose the periodical termination of differential phase trellis at predetermined indices. It is seen that accelerated weight degeneracy and impoverished trajectory diversity - problems that are encountered in traditional SMC methods - are mitigated. Using these observations, a novel SMC framework that circumvents resampling is then developed. The effect of varying termination periods on the performance of the non-resampling detector is investigated. We also present results which show that periodic termination helps to retard weight degeneracy. The performance of traditional and non-resampling SMC detectors for a convolutional-coded OFDM system is compared and simulation results suggest that the nonresampling detector performs better than its traditional counter-part. We also consider a low-density parity check (LDPC)-coded OFDM system and simulation results suggest the near bound performance of the proposed non-resampling SMC detector. © 2006 IEEE.