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We study the effect of fading in the communication channels between sensor nodes on the performance of incremental least mean square (ILMS) algorithm, and derive steady state performance metrics, including the mean-square deviation (MSD), excess mean-square error (EMSE) and mean-square error (MSE). We obtain conditions for mean convergence of the ILMS algorithm and show that in the presence of fading...
The focus of this paper is on multitask learning over adaptive networks where different clusters of nodes have different objectives. We propose an adaptive regularized diffusion strategy using Gaussian kernel regularization to enable the agents to learn about the objectives of their neighbors and to ignore misleading information. In this way, the nodes will be able to meet their objectives more accurately...
Recently, adaptive networks have been widely considered as a powerful solution to distributed information processing problems. In this paper, we provide some insights into the steady-state performance of diffusion least-mean-squares (D-LMS) adaptive network with noisy links. To this end, we define the concept of cooperation gain in adaptive networks. Then, we show that when the connecting links are...
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