A high-efficiency Monte Carlo receiver for digital communications

Stochastic Bayesian detection has recently emerged as a competitive receiver design paradigm for wireless communications applications. It uses Monte Carlo simulations to perform Bayesian inference of a probabilistically modeled communication system so as to obtain the maximum a posterior (MAP) symbol detection and/or channel estimation results. The Monte Carlo concept is attractive in that it is intuitive, optimal, and works for generic Bayesian network structures. However, conventional Monte Carlo methods suffer from poor convergence especially when there is less likely evidence in the collected samples, in which case the simulations are wasted in sample spaces that contribute little to the inference estimates. We present an adaptive sampling method in which the sample allocation process is optimized for efficient MAP detection. It is then demonstrated that this optimized adaptive sampling method can be applied to wireless communication systems for high-efficiency symbol detection and channel estimation. The effectiveness of the derived blind Bayesian multiuser detection is verified by computer simulations.