Large System Decentralized Detection Performance Under Communication Constraints

TL;DR

This paper analyzes the performance of large decentralized sensor networks under power and bandwidth constraints, demonstrating that aggregating many moderate decisions outperforms relying on few high-quality ones.

Contribution

It derives asymptotic detection performance for large sensor systems with non-orthogonal communication, using random matrix theory, under combined power and bandwidth limitations.

Findings

Aggregating many moderate decisions yields better detection performance.

Non-orthogonal communication via DS-CDMA is effective under constraints.

Asymptotic analysis guides sensor decision strategies in large networks.

Abstract

The problem of decentralized detection in a sensor network subjected to a total average power constraint and all nodes sharing a common bandwidth is investigated. 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). In the case of large sensor systems and random spreading, the asymptotic decentralized detection performance is derived assuming independent and identically distributed (iid) sensor observations via random matrix theory. The 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.