Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications

• URL: http://arxiv.org/abs/2109.08139v1
• Date: Thu, 16 Sep 2021 17:54:16 GMT
• Title: Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications
• Authors: Brian Kim and Yi Shi and Yalin E. Sagduyu and Tugba Erpek and Sennur Ulukus
• Abstract summary: We consider adversarial machine learning based attacks on power allocation where the base station (BS) allocates its transmit power to multiple subcarriers. We show that adversarial attacks are much more effective than the benchmark attack in terms of reducing the rate of communications.
• Score: 45.24732440940411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 allocated transmit powers as the output. While the BS allocates the transmit power 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 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.

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