Beam Selection and Power Allocation: Using Deep Learning for Sensing-Assisted Communication
Leyan Chen, Kai Liu, Zhibo Zhang, Baoqi Li
Abstract
Sensing-assisted communication is a feasible technology for the rapid development of 5G and 6G communication systems, which can achieve greater channel capacity with limited energy consumption. In this letter, we propose a deep learning-based beam selection and power allocation algorithm (DL-BSPA), which includes a beam selection network and a power allocation network. The beam selection network uses real millimeter wave radar data to learn how to accurately and efficiently select beams. An unsupervised power allocation network learns and optimizes the power allocation based on the results of the beam selection network. In addition, the DL-BSPA algorithm adopts the joint deployment optimization method to maximize the channel capacity between mobile users and base stations (BS). Simulation results show that the proposed scheme is robust and can achieve competitive performance.