Measurement Matrix Design for Compressive Detection with Secrecy Guarantees

TL;DR

This paper develops measurement matrix designs for compressive detection systems that optimize detection performance while ensuring physical layer secrecy against eavesdroppers, considering various signal models.

Contribution

It introduces optimized measurement matrix design strategies for compressive detection with secrecy guarantees across different signal scenarios.

Findings

Optimized measurement matrices enhance detection performance.

Artificial noise injection improves security levels.

Designs are effective for known, subspace, and sparse signals.

Abstract

In this letter, we consider the problem of detecting a high dimensional signal based on compressed measurements with physical layer secrecy guarantees. We assume that the network operates in the presence of an eavesdropper who intends to discover the state of the nature being monitored by the system. We design measurement matrices which maximize the detection performance of the network while guaranteeing a certain level of secrecy. We solve the measurement matrix design problem under three different scenarios: $a)$ signal is known, $b)$ signal lies in a low dimensional subspace, and $c)$ signal is sparse. It is shown that the security performance of the system can be improved by using optimized measurement matrices along with artificial noise injection based techniques.