Energy-efficient wireless network control via spatio-temporal deep learning and multi-agent reinforcement learning

The rapid expansion of wireless data traffic is placing increasing strain on the energy consumption of current communication networks, intensifying the tension between performance and sustainability objectives. In interference-intensive multiple access scenarios such as power-domain non-orthogonal multiple access (NOMA), energy-efficient optimization is particularly challenging due to the strong coupling between power control and resource allocation decisions. In order to solve this issue, this paper introduces an AI-Enhanced Energy Optimization Framework (AEEOF), which uses deep spatio-temporal learning and reinforcement learning to provide adaptive and energy-aware network control. The proposed framework incorporates a Spatio-Temporal Graph Convolutional Network (ST-GCN) to learn spatial interference relationships and a Gated Recurrent Unit (GRU) to capture temporal traffic dynamics, embedding the resulting representations into a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) controller to support sequential decision-making. Such a design provides the framework with the ability to dynamically distribute power and timing policies based on changes in network conditions. Extensive simulations in a realistic 5G-oriented environment of high interference levels prove the significant performance improvement. The suggested solution can save up to 15% of energy and make the energy use more efficient by about 40%, which is the number of bits delivered per joule. The overall system throughput goes up by 6.25%, the cell-edge user data rate goes up by up to 60%, the fairness goes up by 20%, and the chance of an outage goes down by 70%. A systematic ablation study with three architectural variants validates the individual contribution of each core component - the ST-GCN spatial module, the GRU temporal module, and the MADDPG reinforcement learning controller. Comparative evaluation against conventional orthogonal and non-AI-assisted baselines further supports the effectiveness of the proposed framework within the studied setting. These findings indicate that intelligent spatio-temporal learning is a promising approach for improving energy efficiency and network performance in interference-intensive wireless environments, as demonstrated within the studied 5G-oriented simulation setting.