Large Sensor System Performance of Decentralized Detection in Noisy, Bandlimited Channels

Abstract

In this paper we investigate the performance of decentralized detection assuming a noisy and bandlimited channel between the local sensors and the fusion center. We formulate the problem as in a distributed wireless sensor network subjected to a total average power constraint and all nodes sharing a common bandwidth. The bandwidth constraint is taken into account by assuming non-orthogonal communication between sensors and the data fusion center via direct-sequence code-division multiple-access (DS-CDMA). To facilitate the closed-form analysis we consider large sensor systems and random spreading, and derive the decentralized detection performance assuming independent and identically distributed (iid) sensor observations. For the first time, we apply random matrix theory to obtain decentralized detection performance in large sensor networks. Our results show that, even under both power and bandwidth constraints, it is better to combine many not-so-good local decisions rather than relying on one (or a few) very-good local decisions.