Research on the new paradigm for optimizing 5G base station energy-saving control strategies utilizing the Decision Transformer model

XIA Pengcheng; ZHU Yintao; YU Xiao; HE Yi; NI Yiyang

doi:10.11959/j.issn.2096-3750.2025.00481

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Research on the new paradigm for optimizing 5G base station energy-saving control strategies utilizing the Decision Transformer model

Internet of Energy Things | 更新时间：2025-09-12

- Research on the new paradigm for optimizing 5G base station energy-saving control strategies utilizing the Decision Transformer model
- Chinese Journal on Internet of Things Vol. 9, Issue 2, Pages: 82-94(2025)
- 作者机构：
  
  1.南京理工大学电子工程与光电技术学院，江苏南京 210094
  2.南京理工大学网络空间安全学院，江苏江阴 214443
  3.江苏第二师范学院计算机工程学院，江苏南京 211200
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62471204);The Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu(BK20212001);Major Natural Science Foundation of the Higher Education Institutions of Jiangsu Province(24KJA510003)
- DOI：10.11959/j.issn.2096-3750.2025.00481
  CLC： TN929.53
- Received：04 January 2025，
  
  Revised：2025-02-19，
  
  Published：10 June 2025
- 稿件说明：
移动端阅览
夏鹏程,朱银涛,于霄等.基于Decision Transformer模型的5G基站节能控制策略优化新范式研究[J].物联网学报,2025,09(02):82-94.

XIA Pengcheng,ZHU Yintao,YU Xiao,et al.Research on the new paradigm for optimizing 5G base station energy-saving control strategies utilizing the Decision Transformer model[J].Chinese Journal on Internet of Things,2025,09(02):82-94.
夏鹏程,朱银涛,于霄等.基于Decision Transformer模型的5G基站节能控制策略优化新范式研究[J].物联网学报,2025,09(02):82-94. DOI： 10.11959/j.issn.2096-3750.2025.00481.

XIA Pengcheng,ZHU Yintao,YU Xiao,et al.Research on the new paradigm for optimizing 5G base station energy-saving control strategies utilizing the Decision Transformer model[J].Chinese Journal on Internet of Things,2025,09(02):82-94. DOI： 10.11959/j.issn.2096-3750.2025.00481.

摘要

随着5G网络的持续深入应用，基站设备的增长势头迅猛，这不仅催生了通信行业对提升5G基站综合能效和实现节能减排的新需求，也对相关厂商提出了更高的要求。虽然传统的强化学习（RL

reinforcement learning）技术有望优化5G基站的节能策略，但此类方法需要大量的环境互动和模型训练时间。此外，面对动态变化的基站运行环境，状态和动作空间的变化也会导致RL难以学习到较好的策略，且传统RL模型的泛化能力也有限。为了解决这些问题，创新性地提出了基于Decision Transformer（DT）模型的5G基站节能控制策略优化新框架，该框架将每个基站对应的状态和动作空间解耦，以适应不同的场景任务，并且基于轨迹先验改进了原始DT模型，通过轨迹数据的先验信息来优化模型的期望回报。仿真实验结果表明，所提方法与其他RL算法相比，可以在保障用户服务质量的前提下大幅降低系统功耗，且能够在不重新训练的情况下适应未知任务，所提方法在5G基站节能决策场景中展现出明显优势和应用潜力。

Abstract

With the ongoing proliferation of 5G networks

there has been a surge in the deployment of base station equipment. This trend has not only given rise to new demands within the telecommunications industry for enhancing the overall energy efficiency of 5G base stations and achieving energy conservation and emission reduction but also imposed higher standards on related manufacturers. While traditional reinforcement learning (RL) techniques hold promise for optimizing energy-saving strategies for 5G base stations

they require extensive environmental interactions and model training time. Moreover

in the face of dynamically changing base station operating environments

the variability in state and action spaces can make it difficult for RL to learn effective strategies

and the generalization ability of traditional RL models is also limited. To address these challenges

a new framework for optimizing 5G base station energy-saving control strategies based on the Decision Transformer (DT) model was innovatively proposed. The framework decouples the state and action spaces corresponding to each base station for different scenario tasks and improves the original DT model based on the trajectory priors to optimize the expected return of the model through the prior information of the trajectory data. Simulation results demonstrate that compared to other RL algorithms

the proposed method can significantly reduce system power consumption while ensuring the quality of service for users

and it can adapt to unknown tasks without retraining

showcasing the distinct advantages and application potential of our approach in the context of 5G base station energy-saving decision-making.

关键词

Keywords

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