Extremum-Based Joint Compression and Detection for Distributed Sensing

2026-03-29

AI Summary

This work addresses the fundamental challenge of efficiently determining whether observations from two distributed sensors originate from the same underlying signal under extremely low communication constraints—specifically, when each sensor is limited to transmitting only k bits, or even a single bit. To this end, the paper proposes a novel joint compression-and-detection scheme based on extreme-value sampling: each sensor transmits only the index of its largest observed sample, and the fusion center makes a decision via a simple scalar threshold test. This approach uniquely integrates extreme-value sampling with ultra-low-rate communication, yielding a low-complexity cooperative detection method with analytically tractable, non-asymptotic performance guarantees. The authors derive exact non-asymptotic expressions for both false alarm and miss detection probabilities, and numerical simulations confirm the accuracy of the theoretical analysis and the efficacy of the proposed method.

Abstract

We study joint compression and detection in distributed sensing systems motivated by emerging applications such as IoT-based localization. Two spatially separated sensors observe noisy signals and can exchange only a $k$-bit message over a reliable one-way low-rate link. One sensor compresses its observation into a $k$-bit description to help the other decide whether their observations share a common underlying signal or are statistically independent. We propose a simple extremum-based strategy, in which the encoder sends the index of its largest sample and the decoder performs a scalar threshold test. We derive exact nonasymptotic false-alarm and misdetection probabilities and validate the analysis with representative simulations.

Problem

• distributed sensing
• joint compression and detection
• low-rate communication
• signal correlation detection
• IoT-based localization

Innovation

• extremum-based
• joint compression and detection
• distributed sensing
• nonasymptotic analysis
• low-rate communication