Low Overhead Beam Alignment for Mobile Millimeter Channel Based on Continuous-Time Prediction

In millimeter-wave (mmWave) communications, directional transmission based on beamforming is important to compensate for high pathloss. To maintain the desired directional transmission gain, one standard method is beam scanning, which involves the transmitter sending the pilot signal over all available beam directions to find the optimal one. Alternatively, beam tracking using statistical models and Kalman filter (KF) can save beam training overhead. Unfortunately, existing beam tracking solutions are limited to a fixed beam variation pattern. In this work, we propose an adaptive online beam alignment (AOBA) to reduce beam alignment overhead and achieve accurate beam alignment for any movement profile. The proposed AOBA periodically performs beam tracking using a small amount but carefully selected candidate beams and switches to beam scanning using all available beams based on a given switching rule. During the interval without the pilot signal, the optimal beam at an arbitrary time instant is predicted with the aid of the recently proposed ordinary differential equation (ODE)-long short-term memory (LSTM) model. Extensive simulations are conducted to evaluate the performance of the proposed AOBA in comparison with several existing beam alignment schemes.