Analysis of a Reduced-Communication Diffusion LMS Algorithm
This work addresses communication efficiency in distributed estimation for adaptive networks, but it is incremental as it builds on existing diffusion LMS methods.
The paper tackles the problem of high communication costs in diffusion-based adaptive networks by proposing a reduced-communication diffusion LMS algorithm that allows nodes to receive estimates from only a subset of neighbors, achieving stable convergence and a trade-off between performance and communication cost, with simulation results matching theoretical predictions.
In diffusion-based algorithms for adaptive distributed estimation, each node of an adaptive network estimates a target parameter vector by creating an intermediate estimate and then combining the intermediate estimates available within its closed neighborhood. We analyze the performance of a reduced-communication diffusion least mean-square (RC-DLMS) algorithm, which allows each node to receive the intermediate estimates of only a subset of its neighbors at each iteration. This algorithm eases the usage of network communication resources and delivers a trade-off between estimation performance and communication cost. We show analytically that the RC-DLMS algorithm is stable and convergent in both mean and mean-square senses. We also calculate its theoretical steady-state mean-square deviation. Simulation results demonstrate a good match between theory and experiment.