Informed Scheduling by Stochastic Residual Belief Propagation in Distributed Wireless Networks

This letter devises a novel algorithm for cooperative spectrum sensing based on belief propagation (BP) for distributed wireless networks. The algorithm, called stochastic residual belief propagation (SR-BP), extends the use of residual belief propagation (R-BP) to distributed networks, improving the accuracy, convergence rate, and communication cost for cooperative spectrum sensing. We demonstrate that SR-BP converges to a unique fixed point under conditions similar to those ensuring convergence of asynchronous BP. Then, we develop a way to derive a probability distribution from the residual of each message. Finally, we provide numerical results to showcase the improvements in convergence speed, message overhead and detection accuracy of SR-BP

Merging Belief Propagation and the Mean Field Approximation: A Free Energy Approach

We present a joint message passing approach that combines belief propagation and the mean field approximation. Our analysis is based on the region-based free energy approximation method proposed by Yedidia et al. We show that the message passing fixed-point equations obtained with this combination correspond to stationary points of a constrained region-based free energy approximation. Moreover, we present a convergent implementation of these message passing fixedpoint equations provided that the underlying factor graph fulfills certain technical conditions. In addition, we show how to include hard constraints in the part of the factor graph corresponding to belief propagation. Finally, we demonstrate an application of our method to iterative channel estimation and decoding in an orthogonal frequency division multiplexing (OFDM) system

Sigma Point Belief Propagation

The sigma point (SP) filter, also known as unscented Kalman filter, is an attractive alternative to the extended Kalman filter and the particle filter. Here, we extend the SP filter to nonsequential Bayesian inference corresponding to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a low-complexity approximation of the belief propagation (BP) message passing scheme. SPBP achieves approximate marginalizations of posterior distributions corresponding to (generally) loopy factor graphs. It is well suited for decentralized inference because of its low communication requirements. For a decentralized, dynamic sensor localization problem, we demonstrate that SPBP can outperform nonparametric (particle-based) BP while requiring significantly less computations and communications.Comment: 5 pages, 1 figur