Adversarial Robustness of Lightweight Channel Estimators for Secure 6G Edge Networks

As sixth-generation (6 G) networks transition toward edge-intelligence architectures, deep learning (DL)-based channel estimators face critical security vulnerabilities from adversarial perturbations. This paper investigates the adversarial robustness of a lightweight distilled channel estimator (D-CE) framework designed specifically for resource-constrained 6G edge devices. We systematically evaluate D-CE's resilience against four gradientbased adversarial attacks: Basic Iterative Method (BIM), Fast Gradient Sign Method (FGSM), Momentum Iterative Method (MIM), and Projected Gradient Descent (PGD). Experimental results demonstrate that defensive distillation achieves a 14-fold reduction in computational complexity while reducing attack success rate (ASR) by$\mathbf{2 5}-\mathbf{4 0 \%}$compared to undefended baselines. The D-CE framework maintains normalized mean squared error (NMSE) within$\mathbf{1} \boldsymbol{-} \mathbf{2 ~ d B}$of clean baseline performance under moderate adversarial perturbations, significantly outperforming convolutional neural networks (CNNs) that experience catastrophic estimation failures. These findings position defensive distillation as a practical and effective defense mechanism for secure channel state information (CSI) acquisition in adversarial 6G environments.