Distributed Binary Detection Over Fading Channels: Cooperative And Parallel Architectures

Keywords

Correlation; Distributed detection; Diversity; Error floor; Fusion and sensor rule; Parallel architecture; Space-time coding

Abstract

This paper considers the problem of binary distributed detection of a known signal in correlated Gaussian sensing noise in a wireless sensor network, where sensors are restricted to using the likelihood ratio test (LRT) and communicating with the fusion center (FC) over bandwidth-constrained channels that are subject to fading and noise. To mitigate the deteriorating effect of fading encountered in the conventional parallel fusion architecture, in which sensors directly communicate with the FC, we propose new fusion architectures that enhance the detection performance, via harvesting cooperative gain (so-called "decision diversity gain"). In particular, we propose 1) cooperative fusion architecture with Alamouti's space-time coding scheme at sensors, 2) cooperative fusion architecture with signal fusion at sensors, and 3) parallel fusion architecture with local threshold changing at sensors. For these schemes, we derive the LRT andmajority fusion rules at the FC and provide upper bounds on the average error probabilities for homogeneous sensors, subject to uncorrelated Gaussian sensing noise, in terms of signal-to-noise ratio (SNR) of communication and sensing channels. Our simulation results indicate that when the FC employs the LRT rule, except for low communication SNR and moderate/high sensing SNR, performance improvement is feasible with the new fusion architectures. When the FC utilizes the majority rule, such an improvement is possible, except for high sensing SNR.

Publication Date

9-1-2016

Publication Title

IEEE Transactions on Vehicular Technology

Volume

65

Issue

9

Number of Pages

7090-7109

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TVT.2015.2497266

Socpus ID

84990949424 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84990949424

This document is currently not available here.

Share

COinS