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

Authors

  • Khaydaraliyeva Khilola Farhod qizi,Ergashova Durdona Khusniddin kizi Tashkent University of Information Technologies named after Muhammad al Khwarazmiy Assistent,Tashkent University of Information Technologies named after Muhammad al Khwarazmiy 3rd year student of the Faculty of Mobile Communication Technology

Keywords:

5G, Energy Efficiency, Base Stations, AI Optimization, Reinforcement Learning, Green Telecom

Abstract

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.

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References

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Published

2026-04-01

How to Cite

Khaydaraliyeva Khilola Farhod qizi,Ergashova Durdona Khusniddin kizi. (2026). ENERGY EFFICIENCY IN TELECOM NETWORKS: AI-OPTIMIZED BASE STATION DESIGN AND CHALLENGES. Journal of Applied Science and Social Science, 16(4), 3–6. Retrieved from https://www.internationaljournal.co.in/index.php/jasass/article/view/3905