Energy Prediction Network-based Beam Selection for mmWave Communication Scenarios

The quality and reliability of millimeter wave (mmWave) communication systems heavily rely on the effectiveness and efficiency of beam alignment. The paper presents a rapid beam alignment method based on energy prediction network (EPN) for dynamic mmWave communications. The proposed approach transforms the process of beam selection into a beam energy prediction (BEP) problem, eliminating the need for prior knowledge of channel conditions and requiring only a limited number of active beam energy samples in the selection process. The novel multi-layer perceptron (MLP) framework employed in this study is trained on data generated through the ray tracing (RT) method in dynamic scenarios. Simulation results demonstrate that the proposed beam alignment approach exhibits superior performance with respect to accuracy, cost-effectiveness, and robustness.