Diffusion Adaptation Over Clustered Multitask Networks Based on the Affine Projection Algorithm
This work addresses the need for efficient multi-task estimation in distributed networks, offering incremental improvements over existing single-task methods for applications requiring collaborative parameter estimation.
The paper tackles the problem of estimating multiple parameter vectors simultaneously in distributed adaptive networks by proposing multi-task diffusion strategies based on the Affine Projection Algorithm, which improves robustness against correlated inputs and enhances convergence rate and steady-state error performance as verified through simulations.
Distributed adaptive networks achieve better estimation performance by exploiting temporal and as well spatial diversity while consuming few resources. Recent works have studied the single task distributed estimation problem, in which the nodes estimate a single optimum parameter vector collaboratively. However, there are many important applications where the multiple vectors have to estimated simultaneously, in a collaborative manner. This paper presents multi-task diffusion strategies based on the Affine Projection Algorithm (APA), usage of APA makes the algorithm robust against the correlated input. The performance analysis of the proposed multi-task diffusion APA algorithm is studied in mean and mean square sense. And also a modified multi-task diffusion strategy is proposed that improves the performance in terms of convergence rate and steady state EMSE as well. Simulations are conducted to verify the analytical results.