Fast Adaptive Intelligent Beam Training Method for mmWave Networks

In this study, we consider a downlink millimeter wave (mmWave) transmission model with the objective of efficiently reducing beam training overhead and maximizing long-term average spectral efficiency. We propose a fast adaptive intelligent beam training algorithm based on a model-agnostic meta-reinforcement learning framework to interactively extract statistical information from mmWave environments and promptly detect beam failure by leveraging the spatial sparsity of mmWave channels. Simulation results demonstrate that the proposed algorithm exhibits rapid adaptability to dynamic communication environments and significantly enhances the spectral efficiency compared to existing algorithms.