Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis

TLDR

Individual nodes are equipped with local learning abilities. The study formulates and analyzes distributed estimation algorithms using diffusion protocols to enable cooperation among adaptive nodes. The algorithms compute local parameter estimates and exchange them with neighboring nodes, forming peer‑to‑peer diffusion protocols. The diffusion algorithm is distributed, cooperative, and reacts in real time, yielding lower transient and steady‑state mean‑square error than noncooperative schemes, with closed‑form performance expressions that agree closely with simulations.

Abstract

We formulate and study distributed estimation algorithms based on diffusion protocols to implement cooperation among individual adaptive nodes. The individual nodes are equipped with local learning abilities. They derive local estimates for the parameter of interest and share information with their neighbors only, giving rise to peer-to-peer protocols. The resulting algorithm is distributed, cooperative and able to respond in real time to changes in the environment. It improves performance in terms of transient and steady-state mean-square error, as compared with traditional noncooperative schemes. Closed-form expressions that describe the network performance in terms of mean-square error quantities are derived, presenting a very good match with simulations.