Optimal Precoding Design and Power Allocation for Decentralized Detection of Deterministic Signals

TL;DR

This paper investigates optimal precoding and power allocation strategies in decentralized wireless sensor networks for detecting known deterministic signals, aiming to enhance detection performance under power constraints.

Contribution

It introduces optimal linear precoding design and power allocation methods, analyzes detection performance with varying sensor numbers, and proposes the concept of detection outage for reliability assessment.

Findings

Optimal precoding improves detection accuracy.

Power allocation significantly affects detection performance.

Detection outage quantifies system reliability.

Abstract

We consider a decentralized detection problem in a power-constrained wireless sensor networks (WSNs), in which a number of sensor nodes collaborate to detect the presence of a deterministic vector signal. The signal to be detected is assumed known \emph{a priori}. Given a constraint on the total amount of transmit power, we investigate the optimal linear precoding design for each sensor node. More specifically, in order to achieve the best detection performance, shall sensor nodes transmit their raw data to the fusion center (FC), or transmit compressed versions of their original data? The optimal power allocation among sensors is studied as well. Also, assuming a fixed total transmit power, we examine how the detection performance behaves with the number of sensors in the network. A new concept "detection outage" is proposed to quantify the reliability of the overall detection system. Finally, decentralized detection with unknown signals is studied. Numerical results are conducted to corroborate our theoretical analysis and to illustrate the performance of the proposed algorithm.