An Iterative Scheme for Computing Combination Weights in Diffusion Wireless Networks

In this letter, we propose an iterative scheme for computing combination matrices of adaptive networks used in diffusion of data among nodes. In particular, we study the performance of diffusion least-mean-squares algorithms over wireless networks for different choices of left-stochastic combination matrices. We formulate a convex optimization problem using an upper bound of the mean-squares deviation of the network and obtain a closed-form expression to find these matrices. We also propose an adaptive scheme to obtain the optimal weights that runs in real-time along with the estimation process over the network. Numerical experiments support the theoretical findings of this letter.

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