Distributed M-ary hypothesis testing for decision fusion in multiple-input multipleoutput wireless sensor networks
Distributed M-ary hypothesis testing for decision fusion in multiple-input multipleoutput wireless sensor networks
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Date
2020-11-17
Authors
Jamoos, Ali
Abuawwad, Rushdi
Journal Title
Journal ISSN
Volume Title
Publisher
The Institution of Engineering and Technology
Abstract
In this study, the authors study binary decision fusion over a shared Rayleigh fading channel with multiple antennas at
the decision fusion centre (DFC) in wireless sensor networks. Three fusion rules are derived for the DFC in the case of
distributed M-ary hypothesis testing, where M is the number of hypothesis to be classified. Namely, the optimum maximum a
posteriori (MAP) rule, the augmented quadratic discriminant analysis (A-QDA) rule and MAP observation bound. A comparative
simulation study is carried out between the proposed fusion rules in-terms of detection performance and receiver operating
characteristic (ROC) curves, where several parameters are taken into account such as the number of antennas, number of local
detectors, number of hypothesis and signal-to-noise ratio. Simulation results show that the optimum (MAP) rule has better
detection performance than A-QDA rule. In addition, increasing the number of antennas will improve the detection performance
up to a saturation level, while increasing the number of the hypothesis will deteriorate the detection performance.