Performance Analysis of Likelihood-Based Multiple Access for Detection Over Fading Channels

In this paper, we consider the binary hypothesis testing problem using wireless sensor networks. We analyze the case where sensors transmit their local log-likelihood ratio (LLR) directly to a fusion center (FC) using an analog transmission scheme over multiple-access fading channels. Due to the nature of the wireless medium, the FC receives a superposition of sensor transmissions. The decision is made by the FC and is based on received data from the sensors. In contrast to the case of identical channels and i.i.d observations, the analog transmission of the LLR over multiple-access fading channels does not achieve the centralized error exponent. Large deviation tools are used in this paper to characterize the error exponent in the asymptotic regime (when the number of sensors approaches infinity) in the case of non-i.i.d observations and non-i.i.d fading channels. Chernoff bounding techniques are used to provide bounds on the error probability for a finite number of sensors when the observations and the fading channels are independent across sensors. Specific performance analysis is provided for detection over both i.i.d and spatially correlated Markovian fading channels. Simulation results then illustrate the detector´s performance.