Belief propagation (BP) is considered as a prominent information processing framework for wireless networks recently. However, infeasible computation and communication requirement involved in applications entailing non-discrete distributions limits its use in practical situations with resource constraints. In this paper, based on some previous work, we further investigate an effective approach to address the message representation/approximation problem in BP algorithms, exploiting the recently proposed Gaussian particle filtering technique. The effectiveness of our approach is testified through the dynamic self-calibration problem in wireless networks. This framework can also be readily extended to address various applications in general distributed networks.
['Yanbing Zhang', 'Huaiyu Dai']
Dynamic Self-Calibration in Collaborative Wireless Networks Using Belief Propagation with Gaussian Particle Filtering