369 research outputs found
Distributed M-ary hypothesis testing for decision fusion in multiple-input multiple output wireless sensor networks
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
Massive MIMO for Wireless Sensing with a Coherent Multiple Access Channel
We consider the detection and estimation of a zero-mean Gaussian signal in a
wireless sensor network with a coherent multiple access channel, when the
fusion center (FC) is configured with a large number of antennas and the
wireless channels between the sensor nodes and FC experience Rayleigh fading.
For the detection problem, we study the Neyman-Pearson (NP) Detector and Energy
Detector (ED), and find optimal values for the sensor transmission gains. For
the NP detector which requires channel state information (CSI), we show that
detection performance remains asymptotically constant with the number of FC
antennas if the sensor transmit power decreases proportionally with the
increase in the number of antennas. Performance bounds show that the benefit of
multiple antennas at the FC disappears as the transmit power grows. The results
of the NP detector are also generalized to the linear minimum mean squared
error estimator. For the ED which does not require CSI, we derive optimal gains
that maximize the deflection coefficient of the detector, and we show that a
constant deflection can be asymptotically achieved if the sensor transmit power
scales as the inverse square root of the number of FC antennas. Unlike the NP
detector, for high sensor power the multi-antenna ED is observed to empirically
have significantly better performance than the single-antenna implementation. A
number of simulation results are included to validate the analysis.Comment: 32 pages, 6 figures, accepted by IEEE Transactions on Signal
Processing, Feb. 201
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