PERFORMANCE LIMITS OF LMS-BASED ADAPTIVE NETWORKS
Distributed and Collaborative Signal Processing
Presented by: Xiaochuan Zhao, Author(s): Xiaochuan Zhao, Ali H. Sayed, University of California Los Angeles, United States
In this work we analyze the mean-square performance of different strategies for adaptation over two-node least-mean-squares (LMS) networks. The results highlight some interesting properties for adaptive networks in comparison to centralized solutions. The analysis reveals that the adapt-then-combine (ATC) adaptive network algorithm can achieve lower excess-mean-square-error (EMSE) than a centralized solution that is based on either block or incremental LMS strategies with the same convergence rate.
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