An Adaptive Algorithm for Sampling over Diffusion Networks with Dynamic Parameter Tuning and Change Detection Mechanisms
Daniel Gilio Tiglea, Renato Candido, Magno T. M. Silva. An Adaptive Algorithm for Sampling over Diffusion Networks with Dynamic Parameter Tuning and Change Detection Mechanisms, Digital Signal Processing v.127, pp.1-18, 2022.
Recently, we proposed a sampling algorithm for diffusion networks that locally adapts the number of nodes sampled according to the estimation error. Thus, it reduces the computational cost associated with the learning task when the error is low in magnitude, e.g., during steady state, and maintains the sampling of the nodes otherwise, which enables fast convergence during the transient. However, its performance depends crucially on the choice of the parameter responsible for penalizing the sampling, which is a function of the variance of the measurement noise across the network. Inappropriate choices affect the tracking capability of the algorithm. In this paper, we propose a different solution, which automatically adjusts its own parameters ased on the noise power estimation. Although its computational cost is slightly increased, this modification removes the need for a priori knowledge of the noise variance across the network, and increases its robustness to the presence of noisy nodes in the network. Furthermore, by implementing an adaptive reset system for the sampling mechanism, we are able to significantly improve the tracking capability of the original algorithm.
Diffusion networks, distributed estimation, adaptive filtering, sampling, censoring.