  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.
