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

arXiv: https://arxiv.org/abs/2101.12090 · HTML: https://mlsanity.com/paper/2101.12090/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 22 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 17 of 17)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [Adversarial Attacks on Deep-Learning Based Radio Signal Classification](https://mlsanity.com/paper/1808.07713/) (2018)
- [Deep Learning for Launching and Mitigating Wireless Jamming Attacks](https://mlsanity.com/paper/1807.02567/) (2018)
- [Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications](https://mlsanity.com/paper/1903.01563/) (2019)
- [CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networks](https://mlsanity.com/paper/1911.11523/) (2019)
- [Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks](https://mlsanity.com/paper/1911.00500/) (2019)
- [Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems](https://mlsanity.com/paper/1902.08391/) (2019)
- [An Iterative BP-CNN Architecture for Channel Decoding](https://mlsanity.com/paper/1707.05697/) (2017)
- [The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection](https://mlsanity.com/paper/1902.10674/) (2019)
- [Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels](https://mlsanity.com/paper/2002.02400/) (2020)
- [Novel Massive MIMO Channel Sounding Data Applied to Deep Learning-based Indoor Positioning](https://mlsanity.com/paper/1810.04126/) (2018)
- [Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning](https://mlsanity.com/paper/1910.10766/) (2019)
- [Deep Learning Power Allocation in Massive MIMO](https://mlsanity.com/paper/1812.03640/) (2018)
- [Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifiers](https://mlsanity.com/paper/2006.14576/) (2020)

## Built on by (most cited first, 11 of 11)

- [Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond](https://mlsanity.com/paper/2103.13989/) (2021)
- [Jamming Attacks on Federated Learning in Wireless Networks](https://mlsanity.com/paper/2201.05172/) (2022)
- [Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning](https://mlsanity.com/paper/2107.12173/) (2021)
- [Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples](https://mlsanity.com/paper/2207.01531/) (2022)
- [Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications](https://mlsanity.com/paper/2109.08139/) (2021)
- [Universal Adversarial Attacks on Neural Networks for Power Allocation in a Massive MIMO System](https://mlsanity.com/paper/2110.04731/) (2021)
- [Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training](https://mlsanity.com/paper/2206.06592/) (2022)
- [Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems](https://mlsanity.com/paper/2311.00207/) (2023)
- [Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack](https://mlsanity.com/paper/2410.01393/) (2024)
- [QUASAR: A Quantum-Classical Neural Network for SAR Satellite Physical-Layer Authentication](https://mlsanity.com/paper/2608.20240/) (2026)
- [Feasible but Not Safe: Constraint Violations and Report-Channel Attacks in Learned Cell-Free ISAC Association](https://mlsanity.com/paper/2609.03147/) (2026)

## Abstract (arXiv)

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.