Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach
Machine Learning
2016-09-21 v2 Machine Learning
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.
Cite
@article{arxiv.1601.06201,
title = {Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach},
author = {Prashant Khanduri and Bhavya Kailkhura and Jayaraman J. Thiagarajan and Pramod K. Varshney},
journal= {arXiv preprint arXiv:1601.06201},
year = {2016}
}