ENERGY EFFICIENCY IN TELECOM NETWORKS: AI-OPTIMIZED BASE STATION DESIGN AND CHALLENGES

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.

电信基础设施，尤其是5G基站的能耗持续攀升，给可持续发展与成本管控带来了严峻挑战。本文提出一种人工智能驱动的优化框架，可在不降低网络性能的前提下降低基站能耗。该框架将深度强化学习（Deep Reinforcement Learning，DRL）与实时流量分析相结合，能够动态管控收发器状态、波束成形模式与功率电平。仿真结果显示，该模型可将能源效率提升38%，同时将服务质量（Quality of Service，QoS）合规率维持在95%以上，证实了其在未来绿色电信网络中的有效性。