Deep Learning for Fast Beam Tracking using RSRP in Millimeter Wave MIMO Systems

Abstract

Compressive channel estimation is an effective way to reduce the number of measurements for fast beam tracking in millimeter wave (mmWave) communications. However in some practical scenarios, the phase of the received signal is difficult to measure, leading to challenges for fast beam tracking, especially in non-line-of-sight (NLoS) channels. In this paper, we propose a novel beam tracking algorithm under random phase offset scenarios using compressive sensing (CS). Unlike traditional algorithms based on complex signal measurements, the proposed algorithm could derive the channel information using reference signal received power (RSRP), without the need of knowledge of the phase. To recover the compressive channel with high precision and low complexity, we design a deep learning beam tracking scheme utilizing a complex-valued auto-encoder. Simulation results show that the proposed scheme can outperform traditional hierarchical search manner under blockage and rotation scenarios.