Error Exponents for Bias Detection of a Correlated Process over a MAC Fading Channel

JUAN AUGUSTO MAYA; LEONARDO REY VEGA; CECILIA GABRIELA GALARZA

Saint Martin

Conferencia; Fifth IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing; 2013

IEEE

In this paper, we analyze a binary hypothesis testingproblem using a wireless sensor network (WSN). Using LargeDeviation Theory (LDT), we compute the exponents of the errorprobabilities for the detection of a constant under a correlatedprocess. Each sensor transmits its local measurement through amultiple-access (MAC) Rician fading channel with a line-of-sight(LOS) component to the fusion center (FC) using an uncodedanalog scheme. The FC decides if the constant is present or not.We examine the behavior of the error exponents as a function ofthe correlation process and the fading LOS component. We alsoshow that this scheme achieves the centralized error exponentswhen the number of sensors approaches infinity even when thefading LOS paths between the sensors and the FC are not sostrong and the underlaying process is correlated. In this way,neither feedback between the FC and the sensors nor cooperationbetween the sensors is necessary to provide a sufficient statisticto the FC.