Robust distributed incremental LMS for parameter estimation using heterogeneous adaptive networks

Adaptive networks include a set of nodes with adaptation and learning abilities for modeling various types of self-organized and complex activities encountered in the real world. This paper presents the effect of heterogeneous distributed incremental LMS algorithm with ideal links on the quality of unknown parameter estimation. In heterogeneous adaptive networks, a fraction of the nodes, defined based on previously calculated SNR, is assumed to be the informed nodes that collect data and perform in-network processing, while the remaining nodes are assumed to be uninformed and only participate in the processing tasks. As our simulation results show, the proposed algorithm not only considerably improves the performance of the Distributed Incremental LMS algorithm in a same condition, but also proves a good accuracy of estimation in cases where some of the nodes make unreliable observations (noisy nodes).