A Speculative GLRT-Backed ApproachRobust Deep Learning-Based Array Processing

TL;DR

This paper introduces a robust array processing framework combining deep learning for fast inference with a GLRT validator for statistical reliability, enhancing security against adversarial interference.

Contribution

It develops a novel adversarially resilient array processing method that integrates DL and GLRT, supported by theoretical analysis of spatial robustness.

Findings

The framework outperforms state-of-the-art baselines in adversarial scenarios.

Second-order statistics of the array are shown to be spatially robust to L-p bounded perturbations.

Empirical results validate the theoretical robustness and effectiveness of the proposed approach.

Abstract

Deep learning (DL) has recently emerged as an efficient approach for array processing tasks such as signal detection and direction of arrival. However, DL models lack statistical guarantees and, moreover, are highly susceptible to adversarial interference, raising security concerns about their reliability in adversarial wireless environments. In this letter, we first show that second-order statistics of the received array are spatially robust to L-p bounded adversarial perturbations. Then, motivated by this theoretical result, we develop an adversarially resilient speculative array processing framework that consists of a low-latency DL classifier backed by a theoretically-grounded generalized likelihood ratio test (GLRT) validator, which operates on the spatial domain of the array, where DL is used for fast speculative inference and later confirmed with the GLRT. Empirical evaluations under multiple L-p bounds, perturbation designs, and perturbation magnitudes corroborate our proposed framework and theoretical findings, demonstrating the superior performance of our proposed approach in comparison to multiple state-of-the-art baselines.