Learning-Aided Beam Management for mmWave High-Speed Railway Networks

Abstract

Beam alignment and tracking for millimeter-wave communication networks in highly mobile scenarios, such as high-speed railway, suffer from large overhead cost and time delay loss. To solve this problem, we propose a learning-aided beam management scheme, which divides the high-dimensional beam prediction procedure into two stages, i.e., parameter estimation and hybrid beamforming. The locations and velocities of the mobile terminals are estimated using the maximum likelihood criterion, and a data fusion module is employed to further improve the estimation accuracy and robustness. Then, the next probable beam directions and the corresponding hybrid precoders are derived based on the estimated parameter set. Numerical simulations show that, the proposed method yields significantly lower overhead cost and time delay compared to the existing beam management scheme.