Bayesian detection for BLAST

Abstract

This work demonstrates the use of the Bayesian methodology for detection in Bell Laboratories Layered Space-Time (BLAST) systems. First, we introduce a procedure for constructing prior distributions and propose the use of two types of prior distributions for the problem. From the corresponding posterior distributions, we obtain the Bayesian linear and decision-feedback detectors and show their equivalence to the popular zero forcing and minimum mean square error (MMSE)-based detectors. Then, we establish an equivalent whitening filter output system model whose unique structure lends itself to constructing a dynamic state space model (DSSM) for BLAST systems, which evolves in space. This DSSM allows for the application of sequential Monte Carlo sampling, or particle filtering (PF), for detection in BLAST systems. We introduce two different particle filtering detectors: the generic particle filtering detector and the stochastic M algorithm. The stochastic M algorithm exploits the discrete nature of the problem in the implementation and, therefore, is much more efficient. Overall, a distinct advantage of the PF detectors is that they can greatly reduce error propagation and thereby achieve near optimum performance. In addition, since they aim at the approximation of the posterior distribution using weighted samples, they can provide soft (probabilistic) information about the unknowns.