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Asynchronously dynamic averaging estimation with communication constraints

The paper concerns with the problem of asynchronous gossip-based dynamic averaging estimation with communication constraints, where each agent estimates the local time-varying parameters individually, then random pairs of connected agents iteratively and locally perform a pairwise average of their estimations through quantized information communication. How quantization affects the evolution of the gossip-based dynamic averaging estimation algorithm is investigated. We prove that the agents' states converge to a random variable that deviates from the average of the estimated parameters. We derive a strong result about the upper bound for the asymptotic mean square error of the states, which just captures effect of the quantized precision and is independent of the network parameters.