Neural Networks for Predicting the Optimal Beamforming Angles for Maximized Overall Wireless Network Capacity

Artificial Intelligence (AI) and Machine Learning (ML) are set to transform wireless communication systems. By using deep neural networks and extensive data, these technologies are improving wireless channel performance, making resource allocation more efficient, and enhancing signal detection. Their impact covers areas like source coding and channel estimation, introducing a new era of efficient and adaptable wireless communication. These advancements speed up networks, improve user experiences, and make networks more reliable. AI and ML are playing a crucial role in shaping the future of mobile and wireless technologies. In this paper, we explore how neural networks, through prediction, help to determine optimal beamforming angles to maximize the total channel capacity between transmitters and receivers.