Distributed average consensus with increased convergence rate

The average consensus problem in the distributed signal processing context is addressed by linear iterative algorithms, with asymptotic convergence to the consensus. The convergence of the average consensus for an arbitrary weight matrix satisfying the convergence conditions is unfortunately slow restricting the use of the developed algorithms in applications. In this paper, we propose the use of linear extrapolation methods in order to accelerate distributed linear iterations. We provide analytical and simulation results that demonstrate the validity and effectiveness of the proposed scheme. Finally, we report simulation results showing that the generalized version of our algorithm, when a grid search for the unknown optimum value of mixing parameter is used, significantly outperforms the optimum consensus algorithm based on weight matrix optimization.