Reducing the System Overhead of Millimeter-Wave Beamforming With Neural Networks for 5G and Beyond

Abstract

To accommodate the rapid change of radio propagation environment for mobile communication scenarios, millimeter-wave beamforming requires instantaneous channel state information (CSI) to update its operational parameters in real time, resulting in heavy system overhead. As the number of antennas increases, the system overhead associated with beam management will increase dramatically. To address this overarching problem, a neural network-aided millimeter-wave beamforming algorithm is proposed in this paper. A new parameter, referred to as “beam adjustment interval”, is proposed to evaluate the beamforming performance. It is defined as the maximum time duration in which the signal-to-interference-plus-noise ratio (SINR) of the user equipment can be maintained above the predefined threshold. Besides, a predictive method of beam adjustment to maximize the beam adjustment interval is developed, which considers the SINR not only at the current location but also future possible locations. Simulation results show that the proposed algorithm can significantly increase beam adjustment interval and reduce the total number of beam adjustments for the moving user equipment, thus reducing the system overhead 41.4% on average over 10 randomly generated test traces.