An incremental LMS network with reduced communication delay

作者：Rastegarnia Amir^*; Khalili Azam; Bazzi Wael M; Sanei Saeid

来源：Signal, Image and Video Processing, 2016, 10(4): 769-775.

DOI：10.1007/s11760-015-0809-x

• Adaptive networks
• Distributed estimation
• DILMS algorithm

Successful implementation of incremental adaptive networks requires that the sampling speed of the network be faster than the time between two consecutive measurements taken by nodes. This issue affects the performance of incremental based algorithm in networks with large number of nodes as well as non-stationary environments. In this paper, we introduce a modified incremental least mean-square (ILMS) algorithm that resolves this problem. In the proposed algorithm, every node updates its local estimate when the measurement data are available (temporal processing). When the estimate from the previous node is also available, an adaptive strategy is used to combine the available estimates to further improve the network learning performance (spatial cooperation). We examine the performance of the proposed algorithm in two scenarios including the network with noisy links and non-stationary environment. Simulation results show the superior performance of the proposed algorithm in comparison with the original ILMS algorithm.

• 出版日期2016-4