Data detection using particle filtering in relay-based communication system with Laplace AR channel model

In this paper, we consider channel modeling and data detection in a communication system where an amplify-and-forward relay is used to transmit data between a receiver and a transmitter in a flat-fading channel. The effective channel, which is the cascade of the transmitter-to-relay and relay-to-receiver Rayleigh channels, is modeled as a first-order Laplace autoregressive (AR) process. We also show that the resulting additive noise of the cascade of the two channels has a normal-Laplace distribution which enables us to derive a better approximation to the likelihood probability distribution function (pdf). Given the channel model and better characterization of the likelihood pdf of the system, we formulate the transmission process as dynamic state-space model, and propose a particle filtering based algorithm for data detection. The effectiveness of the performance of the proposed algorithm is investigated through computer simulations.