Network-Wide Distributed Carrier Frequency Offsets Estimation and Compensation via Belief Propagation

In this paper, we propose a fully distributed algorithm for frequency offsets estimation in decentralized systems. The idea is based on belief propagation, resulting in that each node estimates its own frequency offsets by local computations and limited exchange of information with its direct neighbors. Such algorithm does not require any centralized information processing or knowledge of global network topology, thus is scalable with network size. It is shown analytically that the proposed algorithm always converges to the optimal estimates regardless of network topology. Simulation results demonstrate the fast convergence of the algorithm and show that estimation mean-squared-error at each node approaches the centralized Cramér-Rao bound within a few iterations of message exchange.