Predictive Beam Management with Spatio-Temporal Hyper Networks and Reinforcement Intelligence for Sustainable Massive MIMO Architectures

Massive MIMO is the basis of current 5G and future 6G wireless networks, with antenna gain significant spectral efficiency and network capacity, but is unable to support dynamic environments due to computational complexity, power consumption and CSI overhead. The research presents a single adaptive and energy-efficient beam management system, including hybrid analogue-digital beamforming and advanced intelligence based on learning. This system extends Deep Reinforcement Learning to optimize beams in real-time, graph-based learning to optimize beams selectively by topology, neuro-symbolic policy optimization to predict and study control, and spatio-temporal forecasting using hypernetworks to CSI, which cuts feedback costs by a major factor. The proposed framework reduces redundant CSI updates by dynamically changing beam patterns based on user movement, interference, as well as the changes in traffic conditions, and provides strong signals quality and low-latency adaptation. The main innovation is the combination design of prediction beam adaptation and energy aware CSI management. The performance of simulations shows that spectral efficiency, power and computing scalability have been significantly improved, making the solution a viable and future-proof architecture of intelligent massive MIMO network.