Beam Prediction in Millimeter Wave Communication

Millimeter-wave (mmWave) communication has displayed potential in wireless networks as a strategy for achieving high data rates. Within mmWave communication systems, the utilization of beamforming serves to enhance signal integrity while mitigating the impact of path loss. Focusing mainly on forecasting the Angle of Arrival (AOA) by examining a dataset with factors like the distance between Transmitter and Receiver, Azimuth and Elevation Angles of Departure (AoD) and Arrival (AoA), path, and Frequency, this research delves into the use of Long Short-Term Memory (LSTM) networks for beam prediction in mmWave communication systems. To precisely forecast the AOA based on historical channel state information, the proposed LSTM-based model is practically implemented using MATLAB. The outcomes of the simulations demonstrate the efficacy of the LSTM model in AOA prediction, consequently enhancing beamforming efficiency within millimeter-wave communication systems.