This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE/ACM TRANSACTIONS ON NETWORKING 1 Applications of Belief Propagation in CSMA Wireless Networks

Abstract—“Belief propagation ” (BP) is an efficient way to solve “inference ” problems in graphical models, such as Bayesian networks and Markov random fields. It has found great success in many application areas due to its simplicity, high accuracy, and distributed nature. This paper is a first attempt to apply BP algorithms in CSMA wireless networks. Compared to prior CSMA optimization algorithms such as ACSMA, which are measurement-based, BP-based algorithms are proactive and com-putational, without the need for network probing and traffic measurement. Consequently, BP-based algorithms are not af-fected by the temporal throughput fluctuations and can converge faster. Specifically, this paper explores three applications of BP. 1) We show how BP can be used to compute the throughputs of different links in the network given their access intensities, defined as the mean packet transmission time divided by the mea