Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network

TL;DR

This paper demonstrates that deep learning-based power allocation in massive MIMO networks is vulnerable to adversarial attacks, which can significantly disrupt system performance even with minimal input perturbations.

Contribution

It extends adversarial attack methods to regression tasks in wireless systems and benchmarks their effectiveness against DL-based power allocation in massive MIMO networks.

Findings

White-box attacks cause up to 86% infeasible solutions.

Adversarial attacks are effective with small input perturbations.

Black-box attacks also compromise system performance.

Abstract

Deep learning (DL) is becoming popular as a new tool for many applications in wireless communication systems. However, for many classification tasks (e.g., modulation classification) it has been shown that DL-based wireless systems are susceptible to adversarial examples; adversarial examples are well-crafted malicious inputs to the neural network (NN) with the objective to cause erroneous outputs. In this paper, we extend this to regression problems and show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. Specifically, we extend the fast gradient sign method (FGSM), momentum iterative FGSM, and projected gradient descent adversarial attacks in the context of power allocation in a maMIMO system. We benchmark the performance of these attacks and show that with a small perturbation in the input of the NN, the white-box attacks can result in infeasible solutions up to 86%. Furthermore, we investigate the performance of black-box attacks. All the evaluations conducted in this work are based on an open dataset and NN models, which are publicly available.