Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach

TL;DR

This paper introduces universal collaboration strategies for high-dimensional signal detection in distributed networks, leveraging sparse PCA to optimize spatial collaboration among nodes with limited communication to the Fusion Center.

Contribution

It proposes a novel, universal collaboration strategy framework for deterministic signals, connecting collaboration design with sparse PCA for efficient solutions.

Findings

Optimal collaboration strategies improve detection performance.

The approach is universal for a class of deterministic signals.

Efficient algorithms for strategy design are developed.

Abstract

This paper considers the problem of high dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with the Fusion Center (FC). We design optimal collaboration strategies which are universal for a class of deterministic signals. By establishing the equivalence between the collaboration strategy design problem and sparse PCA, we solve the problem efficiently and evaluate the impact of collaboration on detection performance.