Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach
This work addresses signal detection in distributed networks with sparse communication, offering incremental improvements through a novel method for optimizing collaboration strategies.
The paper tackles high-dimensional signal detection in distributed networks with limited communication to a fusion center, designing universal collaboration strategies for deterministic signals and linking the problem to sparse PCA for efficient solution.
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.