mlsanityData to 14 Oct 2026

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

arXiv 2101.12090 ↗First posted 28 Jan 2021cs.IT cs.LG eess.SPcited by 11

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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.

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

17 of 22 references.

References · 17

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  2. 2014Deep Learning in Neural Networks: An Overviewcited by 770
  3. 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 446
  4. 2017Adversarial Examples: Attacks and Defenses for Deep Learningcited by 351
  5. 2018Adversarial Attacks on Deep-Learning Based Radio Signal Classificationcited by 47
  6. 2018Deep Learning for Launching and Mitigating Wireless Jamming Attackscited by 31
  7. 2019Evaluating Adversarial Evasion Attacks in the Context of Wireless Communicationscited by 28
  8. 2019CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networkscited by 22
  9. 2019Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attackscited by 20
  10. 2019Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systemscited by 19
  11. 2017An Iterative BP-CNN Architecture for Channel Decodingcited by 18
  12. 2019The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detectioncited by 17
5 more
  1. 2020Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channelscited by 17
  2. 2018Novel Massive MIMO Channel Sounding Data Applied to Deep Learning-based Indoor Positioningcited by 16
  3. 2019Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learningcited by 15
  4. 2018Deep Learning Power Allocation in Massive MIMOcited by 13
  5. 2020Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifierscited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 3; 2023: 1; 2024: 1; 2025: 0; 2026: 2.

11 papers.

  1. 2021Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyondcited by 6
  2. 2022Jamming Attacks on Federated Learning in Wireless Networkscited by 6
  3. 2021Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learningcited by 5
  4. 2022Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examplescited by 5
  5. 2021Adversarial Attacks against Deep Learning Based Power Control in Wireless Communicationscited by 4
  6. 2021Universal Adversarial Attacks on Neural Networks for Power Allocation in a Massive MIMO Systemcited by 3
  7. 2022Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Trainingcited by 1
  8. 2023Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systemscited by 1
  9. 2024Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attackcited by 0
  10. 2026QUASAR: A Quantum-Classical Neural Network for SAR Satellite Physical-Layer Authenticationcited by 0
  11. 2026Feasible but Not Safe: Constraint Violations and Report-Channel Attacks in Learned Cell-Free ISAC Associationcited by 0