TY - GEN

T1 - Asymptotically optimal quantization for detection in power constrained decentralized sensor networks

AU - Kashyap, Akshay

AU - Basar, M Tamer

AU - Srikant, Rayadurgam

PY - 2006

Y1 - 2006

N2 - We study a Bayesian decentralized binary hypothesis testing problem, in which N sensors make observations related to a two-valued hypothesis, and send messages based on the observations to a fusion center, with the objective of enabling the fusion center to accurately reconstruct the realized hypothesis. We assume that the observations at the sensors are independent and identically distributed conditioned on the hypothesis, and that the sensors transmit their messages over independent, parallel channels to the fusion center. We also make the natural assumption that the sensors have identical, finite message sets at their disposal, that each message has some cost associated with it, and that there is a constraint on the average cost incurred by a sensor. We study the large N asymptote, and therefore are interested in schemes that optimize the error exponent at the fusion center. Our main contributions are 1) proving that the problem of finding the optimal schemes is a finite dimensional optimization problem, 2) a description of the structure of optimal rules: we prove that optimal sensor rules are randomized likelihood ratio quantizers (LRQs), with randomization being over at most two deterministic LRQs. We further show that under some conditions, randomization is not required.

AB - We study a Bayesian decentralized binary hypothesis testing problem, in which N sensors make observations related to a two-valued hypothesis, and send messages based on the observations to a fusion center, with the objective of enabling the fusion center to accurately reconstruct the realized hypothesis. We assume that the observations at the sensors are independent and identically distributed conditioned on the hypothesis, and that the sensors transmit their messages over independent, parallel channels to the fusion center. We also make the natural assumption that the sensors have identical, finite message sets at their disposal, that each message has some cost associated with it, and that there is a constraint on the average cost incurred by a sensor. We study the large N asymptote, and therefore are interested in schemes that optimize the error exponent at the fusion center. Our main contributions are 1) proving that the problem of finding the optimal schemes is a finite dimensional optimization problem, 2) a description of the structure of optimal rules: we prove that optimal sensor rules are randomized likelihood ratio quantizers (LRQs), with randomization being over at most two deterministic LRQs. We further show that under some conditions, randomization is not required.

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M3 - Conference contribution

AN - SCOPUS:34047205091

SN - 1424402107

SN - 9781424402106

T3 - Proceedings of the American Control Conference

SP - 2060

EP - 2065

BT - Proceedings of the 2006 American Control Conference

T2 - 2006 American Control Conference

Y2 - 14 June 2006 through 16 June 2006

ER -