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UBC Theses and Dissertations

Multiple--symbol differential decision fusion for wireless sensor networks Lei, Andre

Abstract

We consider the problem of decision fusion in mobile wireless sensor networks where the channels between the sensors and the fusion center are time—variant. We assume that the sensors make independent local decisions on the M hypotheses under test and report these decisions to the fusion center using differential phase—shift keying (DPSK), so as to avoid the channel estimation overhead entailed by coherent decision fusion. For this setup we derive the optimal and three low—complexity, suboptimal fusion rules which do not require knowledge of the instantaneous fading gains. Since all these fusion rules exploit an observation window of at least two symbol intervals, we refer to them collectively as multiple—symbol differential (MSD) fusion rules. For binary hypothesis testing, we derive performance bounds for the optimal fusion rule and exact or approximate analytical expressions for the probabilities of false alarm and detection for all three suboptimal fusion rules. Simulation and analytical results confirm the excellent performance of the proposed MSD fusion rules and show that in fast fading channels significant performance gains can be achieve by increasing the observation window to more than two symbol intervals.

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Attribution-NonCommercial-NoDerivatives 4.0 International

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