Deep Learning Assisted Adaptive mmWave Beam Tracking: A Sum-Probability Oriented Methodology

Abstract

In this paper, an adaptive millimeter-wave (mmWave) beam tracking scheme is proposed to flexibly adjust the angular range of beam tracking based on the user-specific speeds for reducing the tracking overhead, where deep learning is exploited to accurately extract the user movement features. Specifically, long short-term memory network is utilized to predict the possible optimal beams according to the received signals of previous beam tracking. Based on the predicted probabilities, the sum-probability criterion is proposed to track the subset of maximum-probability beams whose sum-probability is larger than the predefined threshold, where the beam with the highest received power is selected as the optimal one. Considering the limited number of received beam tracking signals, a two-stage training strategy is further proposed to stabilize the model optimization. Simulation results demonstrate that our proposed scheme could effectively reduce the overhead of beam tracking in guarantee of high beamforming gains, compared with the conventional schemes.