mlsanityData to 14 Oct 2026

Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications

arXiv 2109.08139 ↗First posted 16 Sep 2021eess.SP cs.LG cs.NIcited by 4

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Abstract (arXiv)

We consider adversarial machine learning based attacks on power allocation where the base station (BS) allocates its transmit power to multiple orthogonal subcarriers by using a deep neural network (DNN) to serve multiple user equipments (UEs). The DNN that corresponds to a regression model is trained with channel gains as the input and returns transmit powers as the output. While the BS allocates the transmit powers to the UEs to maximize rates for all UEs, there is an adversary that aims to minimize these rates. The adversary may be an external transmitter that aims to manipulate the inputs to the DNN by interfering with the pilot signals that are transmitted to measure the channel gain. Alternatively, the adversary may be a rogue UE that transmits fabricated channel estimates to the BS. In both cases, the adversary carefully crafts adversarial perturbations to manipulate the inputs to the DNN of the BS subject to an upper bound on the strengths of these perturbations. We consider the attacks targeted on a single UE or all UEs. We compare these attacks with a benchmark, where the adversary scales down the input to the DNN. We show that the adversarial attacks are much more effective than the benchmark attack in terms of reducing the rate of communications. We also show that adversarial attacks are robust to the uncertainty at the adversary including the erroneous knowledge of channel gains and the potential errors in exercising the attacks exactly as specified.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 29 of its 42 references that are in the map. First posted on arXiv 16 Sep 2021.

Builds on

29 of 42 references.

References · 29

  1. 2013Intriguing properties of neural networkscited by 5,155
  2. 2016Adversarial examples in the physical worldcited by 1,769
  3. 2018Adversarial Attacks on Deep-Learning Based Radio Signal Classificationcited by 47
  4. 2020Deep Learning for Wireless Communicationscited by 39
  5. 2018Deep Learning for Launching and Mitigating Wireless Jamming Attackscited by 31
  6. 2019Evaluating Adversarial Evasion Attacks in the Context of Wireless Communicationscited by 28
  7. 2020Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifierscited by 28
  8. 2020Adversarial Machine Learning in Wireless Communications using RF Data: A Reviewcited by 25
  9. 2019Generative Adversarial Network for Wireless Signal Spoofingcited by 21
  10. 2019IoT Network Security from the Perspective of Adversarial Deep Learningcited by 20
  11. 2019Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attackscited by 20
  12. 2019Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systemscited by 19
17 more
  1. 2019The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detectioncited by 17
  2. 2020Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channelscited by 17
  3. 2019Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learningcited by 15
  4. 2019Spectrum Data Poisoning with Adversarial Deep Learningcited by 14
  5. 2020Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofingcited by 14
  6. 2018Deep Learning Power Allocation in Massive MIMOcited by 13
  7. 2020Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifierscited by 13
  8. 2019When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensingcited by 12
  9. 2020When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directionscited by 12
  10. 2021Adversarial Machine Learning for 5G Communications Securitycited by 12
  11. 2021Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Networkcited by 11
  12. 2020Adversarial Machine Learning based Partial-model Attack in IoTcited by 9
  13. 2020Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifierscited by 9
  14. 2020Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifierscited by 9
  15. 2020How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacycited by 7
  16. 2021Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyondcited by 6
  17. 2021Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learningcited by 5

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Cited by year

Citing papers per year: 2021: 2; 2022: 2.

4 papers.

  1. 2022Jamming Attacks on Federated Learning in Wireless Networkscited by 6
  2. 2021Autoencoder-based Communications with Reconfigurable Intelligent Surfacescited by 3
  3. 2022Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Trainingcited by 1
  4. 2021Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfacescited by 0