Delay-Aware and Energy-Efficient Integrated Optimization System for 5G Networks
Tan, Jingchao Zhang, Tiancheng Zhang, Cheng Wang, Chenyang Qiu, Chao Wang, Xiaofei Guizani, Mohsen
Tan, Jingchao
Zhang, Tiancheng
Zhang, Cheng
Wang, Chenyang
Qiu, Chao
Wang, Xiaofei
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
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Language
English
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Abstract
To meet the demands of high-capacity and low-delay services, Fifth Generation (5G) Base Stations (BSs) are typically deployed in ultra-dense configurations, especially in urban areas. While this densification enhances coverage and service quality, it also leads to substantially increased energy consumption. However, the dense deployment pattern makes BS workloads more responsive to the spatiotemporal variations in user behavior, offering opportunities for energy-saving strategies that dynamically adjust BS operation states. In this context, we propose a Delay-aware and Energy-efficient Integrated Optimization System (DEIS) based on Deep Reinforcement Learning (DRL), which jointly optimizes energy consumption and network delay while maintaining user satisfaction. DEIS leverages a real-world dataset collected from operational 5G BSs provided by partner network operators, containing both BS deployment data and high-volume user request logs. Extensive simulations demonstrate that DEIS can achieve a 41% reduction in energy consumption while ensuring reliable delay performance.
Citation
J. Tan et al., "Delay-Aware and Energy-Efficient Integrated Optimization System for 5G Networks," in IEEE Transactions on Network and Service Management, doi: 10.1109/TNSM.2025.3623778.
Source
IEEE Transactions on Network and Service Management
Conference
Keywords
5G Networks, Deep Reinforcement Learning, Delay Aware, Energy Efficient
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Publisher
IEEE