期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 58, 期 3, 页码 1035-1048出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2009.2033729
关键词
Adaptive networks; diffusion LMS; diffusion networks; distributed estimation; energy conservation
资金
- National Science Foundation [ECS-0601266, ECS-0725441, CCF-094936]
- Direct For Computer & Info Scie & Enginr [0942936] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations [0942936] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [GRANTS:13677290] Funding Source: National Science Foundation
We consider the problem of distributed estimation, where a set of nodes is required to collectively estimate some parameter of interest from noisy measurements. The problem is useful in several contexts including wireless and sensor networks, where scalability, robustness, and low power consumption are desirable features. Diffusion cooperation schemes have been shown to provide good performance, robustness to node and link failure, and are amenable to distributed implementations. In this work we focus on diffusion-based adaptive solutions of the LMS type. We motivate and propose new versions of the diffusion LMS algorithm that outperform previous solutions. We provide performance and convergence analysis of the proposed algorithms, together with simulation results comparing with existing techniques. We also discuss optimization schemes to design the diffusion LMS weights.
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