Deep Learning Beam Optimization in Millimeter-Wave Communication Systems
This work addresses efficiency and fairness in millimeter-wave communication systems, representing an incremental improvement by combining existing methods.
The paper tackles the problem of optimizing discrete and continuous variables in millimeter-wave communication systems to fairly allocate user rates, achieving this by predicting beam configurations with a neural network and using a fixed-point algorithm for power allocation and user assignments, which reduces beam search complexity significantly.
We propose a method that combines fixed point algorithms with a neural network to optimize jointly discrete and continuous variables in millimeter-wave communication systems, so that the users' rates are allocated fairly in a well-defined sense. In more detail, the discrete variables include user-access point assignments and the beam configurations, while the continuous variables refer to the power allocation. The beam configuration is predicted from user-related information using a neural network. Given the predicted beam configuration, a fixed point algorithm allocates power and assigns users to access points so that the users achieve the maximum fraction of their interference-free rates. The proposed method predicts the beam configuration in a "one-shot" manner, which significantly reduces the complexity of the beam search procedure. Moreover, even if the predicted beam configurations are not optimal, the fixed point algorithm still provides the optimal power allocation and user-access point assignments for the given beam configuration.