  In distributed multiple-input multiple-output (D-MIMO) networks, power
control is crucial to optimize the spectral efficiencies of users and max-min
fairness (MMF) power control is a commonly used strategy as it satisfies
uniform quality-of-service to all users. The optimal solution of MMF power
control requires high complexity operations and hence deep neural network based
artificial intelligence (AI) solutions are proposed to decrease the complexity.
Although quite accurate models can be achieved by using AI, these models have
some intrinsic vulnerabilities against adversarial attacks where carefully
crafted perturbations are applied to the input of the AI model. In this work,
we show that threats against the target AI model which might be originated from
malicious users or radio units can substantially decrease the network
performance by applying a successful adversarial sample, even in the most
constrained circumstances. We also demonstrate that the risk associated with
these kinds of adversarial attacks is higher than the conventional attack
threats. Detailed simulations reveal the effectiveness of adversarial attacks
and the necessity of smart defense techniques.
