Data detection in relay-based communication systems using Bayesian methods

Data detection in a relay-based communication system (RCS) is challenging because its end-to-end channel, which comprises a cascade of several channels, has unique statistical characteristics. Assuming different channel conditions, in this paper, we address the problem of data detection in an RCS where one amplify-and-forward relay is used as an intermediate node between a transmitter and a receiver. Our approach is based on Bayesian methodologies in which a variant of Markov Chain Monte Carlo (MCMC) technique, known as Metropolis–Hasting-within-Gibbs, is applied for systems with quasi-static channel models, whereas particle filtering technique is used for systems with fast varying channels to develop joint data detection and channel estimation algorithms. By providing detailed derivations, we present two algorithms for each channel condition by formulating the transmission process of the communication systems in different ways. The effectiveness of our algorithms is demonstrated through computer simulations.