Using of Deep Learning in Beamforming Antenna Array

Digital beamforming (DBF) is a crucial technology for large antenna arrays, offering precise control over beam steering. This research introduces a novel method to enhance millimeter wave transmission by incorporating DBF with long-term memory (LSTM) based deep learning Our system utilizes digital signal processing and LSTM networks to optimize beamforming parameters instead of relying on traditional analog beamforming. The objective is to achieve high spectral efficiency. The methodology is executed in the programming language MATLAB and the obtained simulation outcomes validate a substantial enhancement in the metrics that evaluate performance, thereby demonstrating the potential of combining digital beamforming (DBF) with Long Short-Term Memory (LSTM) for forthcoming communication systems. Furthermore, the inclusion of LSTM in the process of digital beamforming presents a comprehensive comprehension of the proposed approach, which imparts valuable insights for advanced communication technologies. To substantiate the efficacy of the technique, several illustrative examples are employed to steer the beam pattern in the desired direction.