ADDRESSING ENERGY EFFICIENCY CHALLENGES IN TELECOM NETWORKS WITH AI-OPTIMIZED BASE STATIONS

The increasing energy consumption of telecom infrastructure, particularly 5G base stations, poses significant sustainability and cost challenges. This paper proposes an AI-driven optimization framework to reduce energy usage in base stations without degrading network performance. By integrating deep reinforcement learning (DRL) with real-time traffic analysis, the system dynamically manages transceiver states, beamforming patterns, and power levels. Simulation results show a 38% improvement in energy efficiency while maintaining over 95% QoS compliance, demonstrating the model's effectiveness in future green telecom networks.