Optimal linear cooperation for signal classification
Zhi Quan, Muyang Ye, Zhi Ding, Shuguang Cui
Abstract
In distributed inference, cooperation among networked agents can be exploited to enhance the performance of each individual agent. In this paper, we consider signal classification over a network of agents, where each agent observes a certain signal under a particular signal-to-noise ratio (SNR). Each agent produces a statistic that summarizes its observations over a time period and then forwards it to a fusion center for identifying the type of signal in a global manner. A linear cooperation strategy for signal classification is formulated as maximizing the classification probability subject to constrained misclassification probabilities. We show that this problem can be transformed into a convex problem under some conditions and linear cooperation is a simple but effective strategy that can greatly enhance the performance of signal classification over networked agents.
BibTeX
@inproceedings{icassp2016_optimallinearcoo,
title = {Optimal linear cooperation for signal classification},
author = {Zhi Quan and Muyang Ye and Zhi Ding and Shuguang Cui},
booktitle = {ICASSP 2016},
year = {2016}
}