Digital beamforming enhancement with LSTM-based deep learning for millimeter wave transmission

Abstract

Abstract Digital beamforming (DBF) has emerged as a pivotal technology for large-scale antenna arrays, offering precise beam steering control. This study presents an innovative approach to enhance millimeter wave transmission by integrating DBF with long short-term memory (LSTM)-based deep learning. Departing from conventional analog beamforming, our proposed system leverages digital signal processing and LSTM networks to optimize beamforming parameters, particularly in the presence of imperfect Channel state information. The primary objective is to achieve heightened spectral efficiency and increased robustness to channel uncertainties. Implemented in MATLAB, our methodology demonstrates significant performance enhancement through simulation results. The findings highlight the potential of DBF with LSTM for future communication systems. Furthermore, the study incorporates LSTM network training on historical data and its integration within the DBF process, offering a comprehensive perspective. This provides a clearer overview of the research issue, key findings, and contributions, setting the stage for the subsequent detailed exploration in the study.