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We develop a new and efficient algorithm to solve the problem of joint channel and Doppler offset estimation in time-varying cooperative wireless relay networks. We first formulate the problem as a Bayesian dynamic nonlinear state space model, then develop an algorithm, which is based on particle adaptive marginal Markov chain Monte Carlo, method to jointly estimate the time-varying channels and static Doppler offsets. We perform detailed complexity analysis of the proposed algorithm and show that it is very efficient and requires moderate computational complexity. In addition, we develop a new version of the recursive marginal Crame&#x0301;r-Rao lower bound and derive expressions for the achievable mean-square error. Simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms and performs close to the Crame&#x0301;r-Rao lower bound.

Joint Channel and Doppler Offset Estimation in Dynamic Cooperative Relay Networks