联合功率控制和信道分配的蜂窝网络能效优化算法

徐会彬

doi:10.11959/j.issn.1000-0801.2024004

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联合功率控制和信道分配的蜂窝网络能效优化算法

研究与开发 | 更新时间：2024-06-05

- 联合功率控制和信道分配的蜂窝网络能效优化算法
- Energy efficiency optimization under joint transmittion power and channel allocation for cellular network
- 电信科学 2024年40卷第2期页码：38-46
- 作者机构：
- 作者简介：
  
  [ "徐会彬（1982- ），男，博士，湖州师范学院信息工程学院讲师，主要研究方向为 VANET安全、路由技术" ]
- 基金信息：
  
  浙江省软科学研究计划项目;Zhejiang Soft Science Research Program(2021C35129);湖州市自然科学基金资助项目;Huzhou Natural Science Foundation Project(2021YZ20)
- DOI：10.11959/j.issn.1000-0801.2024004
  中图分类号： TN929.5
- 网络出版日期：2024-02，
  
  纸质出版日期：2024-02-20
- 稿件说明：
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徐会彬. 联合功率控制和信道分配的蜂窝网络能效优化算法[J]. 电信科学, 2024,40(2):38-46.

Huibin XU. Energy efficiency optimization under joint transmittion power and channel allocation for cellular network[J]. Telecommunications science, 2024, 40(2): 38-46.
徐会彬. 联合功率控制和信道分配的蜂窝网络能效优化算法[J]. 电信科学, 2024,40(2):38-46. DOI： 10.11959/j.issn.1000-0801.2024004.

Huibin XU. Energy efficiency optimization under joint transmittion power and channel allocation for cellular network[J]. Telecommunications science, 2024, 40(2): 38-46. DOI： 10.11959/j.issn.1000-0801.2024004.

摘要

为了提高混合设备到设备（D2D）蜂窝网络中D2D干扰导致系统能效下降的问题，提出了联合功率控制和信道分配的能效优化（EEPC）算法，进而提升系统能效。以 D2D 用户和蜂窝用户最小速率为约束条件，建立最大化能效的优化问题；利用块坐标下降法将优化问题转化为信道分配和功率控制两个子问题，再分别利用Q学习算法、Dinkelbach算法和优化最小（MM）算法求解。并对Q学习算法中贪婪搜索因子进行改进，采用动态的搜索因子，平衡探索与利用间的关系。性能分析表明，提出的 EEPC 算法提升了系统能效。

Abstract

In order to solve problem of low-energy efficiency caused by device-to-device (D2D) interference in hybrid D2D cellular network

energy efficiency improvement based on joint power control and channel allocation (EEPC) algorithm was proposed to improve the energy efficiency.The optimization problem of maximizing energy efficiency was established with the constraint of ensuring the minimum rate of D2D users and cellular users.By using block coordinate descent method

the optimization problem was transformed into two sub-problems of channel allocation and power control

which were solved by Q-learning algorithm

Dinkelbach algorithm and majorization-minimization (MM) respectively.The greedy search factor in Q-learning algorithm was improved

and a dynamic search factor was used to balance the relationship between exploration and exploitation.Performance analysis shows that the proposed EEPC algorithm improves the energy efficiency of the system.

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references

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