Implementation of Adaptive beamforming using Machine Learning techniques in 6G networks

Traditional beamforming techniques rely on optimization or analytically derived weight calculations. Beamforming techniques are often inadequate in dynamic propagation environments characterized by user mobility, rapid channel variations, and heterogeneous service demands. The evolution of 6G wireless communication systems demands intelligent and highly adaptive beamforming mechanisms to satisfy stringent requirements for spectral efficiency, latency, reliability, and massive connectivity. To overcome these limitations, this study presents an artificial intelligence and machine learning-based adaptive beamforming framework tailored for future 6G communication networks. The proposed approach integrates ray-tracing-based channel modeling with supervised learning techniques to enable data-driven-optimized beamforming. Artificial neural network (ANN) and Support Vector Regression (SVR) models directly predict optimal complex-valued beamforming weights from relevant channel features such as angular parameters, user position, transmission power, and path loss. A massive multiple input multiple output (MIMO) configuration using a 32-element uniform linear array and 64 system parameters was considered to evaluate the effectiveness of the learning-based framework under realistic simulation conditions. The results indicate that AI and ML beamforming enable fast and precise beam adaptation while eliminating the need for computationally real-time analytical calculations. The proposed work offers a scalable and efficient solution for intelligent beam management in dynamic 6G environments and supports real-time decision making. Furthermore, this study establishes a strong foundation for future extensions incorporating edge intelligence, federated learning, reinforcement learning, and autonomous beam control in next-generation wireless networks.