Joint Collaboration and Compression Design for Random Signal Detection in Wireless Sensor Networks

In this work, we propose a joint collaboration-compression framework for the random signal detection problem in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a linear collaboration matrix) with each other. Then a subset of sensors linearly compress their aggregated information before communicating with the fusion center (FC). We propose a novel metric called generalized deflection coefficient (GDC) for evaluating the detection performance which is shown to be tightly upper bounded by the Kullback-Leibler divergence for Gaussian observations. We jointly design the linear collaboration and compression strategies under power constraints via alternating maximization of the proposed GDC metric. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.

Automated summary

This work presents a joint collaboration-compression framework for random signal detection in a resource constrained wireless sensor network. The framework involves local sensor collaboration, linear compression of information, and the introduction of a novel metric for evaluating detection performance. The proposed strategies are optimized under power constraints through the use of the generalized deflection coefficient metric.