Distributed Blind Source Separation based on FastICA

TL;DR

This paper introduces a distributed ICA algorithm for wireless sensor networks that avoids network-wide pre-whitening by leveraging the DASF framework, enabling efficient source separation with limited communication.

Contribution

It proposes a novel distributed FastICA method that circumvents the need for network-wide pre-whitening, reducing communication overhead in WSNs.

Findings

Successfully identifies independent source signals in distributed settings.

Reduces communication load linearly with the number of sources.

Maintains accuracy comparable to centralized ICA solutions.

Abstract

With the emergence of wireless sensor networks (WSNs), many traditional signal processing tasks are required to be computed in a distributed fashion, without transmissions of the raw data to a centralized processing unit, due to the limited energy and bandwidth resources available to the sensors. In this paper, we propose a distributed independent component analysis (ICA) algorithm, which aims at identifying the original signal sources based on observations of their mixtures measured at various sensor nodes. One of the most commonly used ICA algorithms is known as FastICA, which requires a spatial pre-whitening operation in the first step of the algorithm. Such a pre-whitening across all nodes of a WSN is impossible in a bandwidth-constrained distributed setting as it requires to correlate each channel with each other channel in the WSN. We show that an explicit network-wide pre-whitening step can be circumvented by leveraging the properties of the so-called Distributed Adaptive Signal Fusion (DASF) framework. Despite the lack of such a network-wide pre-whitening, we can still obtain the $Q$ least Gaussian independent components of the centralized ICA solution, where $Q$ scales linearly with the required communication load.