Energy efficiency optimization under joint transmittion power and channel allocation for cellular network

Huibin XU

doi:10.11959/j.issn.1000-0801.2024004

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Energy efficiency optimization under joint transmittion power and channel allocation for cellular network

Research and Development | 更新时间：2024-06-05

- Energy efficiency optimization under joint transmittion power and channel allocation for cellular network
- Telecommunications Science Vol. 40, Issue 2, Pages: 38-46(2024)
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- DOI：10.11959/j.issn.1000-0801.2024004
  CLC： TN929.5
- Published Online：2024-02，
  
  Published：20 February 2024
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Huibin XU. Energy efficiency optimization under joint transmittion power and channel allocation for cellular network[J]. Telecommunications science, 2024, 40(2): 38-46.
DOI：

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

易鑫 , 裴二荣 . D2D辅助NB-IoT系统的传输设备数优化算法 [J ] . 重庆邮电大学学报(自然科学版) , 2021 , 33 ( 4 ): 606 - 613 .

YI X , PEI E R . Transmission device number optimization algorithm for D2D-assisted NB-IoT system [J ] . Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition) , 2021 , 33 ( 4 ): 606 - 613 .

陶静 , 朱琦 . 基于NOMA的D2D通信联合子信道与功率分配算法 [J ] . 南京邮电大学学报(自然科学版) , 2018 , 38 ( 3 ): 32 - 39 .

TAO J , ZHU Q . Joint subchannel and power allocation algorithm in D2D communication based NOMA [J ] . Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition) , 2018 , 38 ( 3 ): 32 - 39 .

余翔 , 张海波 , 杨路 . 混合D2D蜂窝网络中基于模拟退火算法的资源调度策略 [J ] . 电信科学 , 2017 , 33 ( 4 ): 71 - 77 .

YU X , ZHANG H B , YANG L . Resource scheduling strategy based on simulated annealing algorithm in hybrid D2D cellular networks [J ] . Telecommunications Science , 2017 , 33 ( 4 ): 71 - 77 .

张先超 , 赵耀 , 叶海军 , 等 . 无线网络多用户干扰下智能发射功率控制算法 [J ] . 通信学报 , 2022 , 43 ( 2 ): 15 - 21 .

ZHANG X C , ZHAO Y , YE H J , et al . Intelligent transmit power control algorithm for the multi-user interference of wireless network [J ] . Journal on Communications , 2022 , 43 ( 2 ): 15 - 21 .

CHEN X , HU R Q , JEON J , et al . Energy efficient resource allocation for D2D communication underlaying cellular networks [C ] // Proceedings of the 2015 IEEE International Conference on Communications (ICC) . Piscataway:IEEE Press , 2015 : 2943 - 2948 .

ABRARDO A , MORETTI M . Distributed power allocation for D2D communications underlaying/overlaying OFDMA cellular networks [J ] . IEEE Transactions on Wireless Communications , 2017 , 16 ( 3 ): 1466 - 1479 .

LYU J B , CHEW Y H , WONG W C . A stackelberg game model for overlay D2D transmission with heterogeneous rate requirements [J ] . IEEE Transactions on Vehicular Technology , 2016 , 65 ( 10 ): 8461 - 8475 .

WANG F R , XU C , SONG L Y , et al . Energy-efficient radio resource and power allocation for device-to-device communication underlaying cellular networks [C ] // Proceedings of the 2012 International Conference on Wireless Communications and Signal Processing (WCSP) . Piscataway:IEEE Press , 2012 : 1 - 6 .

杨佳颖 , 李汀 , 解培中 . NOMA增强型D2D通信的资源分配算法 [J ] . 信号处理 , 2021 , 37 ( 8 ): 1441 - 1451 .

YANG J Y , LI T , XIE P Z . NOMA enhanced D2D communication resource allocation algorithm [J ] . Journal of Signal Processing , 2021 , 37 ( 8 ): 1441 - 1451 .

NASIR Y S , GUO D N . Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 10 ): 2239 - 2250 .

王鹏杰 . 基于无线能量传输的无人机协作通信网络性能优化 [D ] . 北京:北京邮电大学 , 2021 .

WANG P J . Performance optimization of UAV cooperative communication network based on wireless energy transmission [D ] . Beijing:Beijing University of Posts and Telecommunications , 2021 .

肖宇 , 邓正宏 . 信杂噪比约束下基于互信息的雷达波形设计 [J ] . 系统工程与电子技术 , 2021 , 43 ( 7 ): 1775 - 1780 .

XIAO Y , DENG Z H . MI-based radar waveform design under SINR constraint [J ] . Systems Engineering and Electronics , 2021 , 43 ( 7 ): 1775 - 1780 .

廖雅婷 . 智能超表面辅助的蜂窝网络多域资源优化技术研究 [D ] . 成都:电子科技大学 , 2022 .

LIAO Y T . Research on multi-domain resource optimization technology of cellular network assisted by intelligent hypersurface [D ] . Chengdu:University of Electronic Science and Technology of China , 2022 .

李威 , 张晓东 , 姜学峰 , 等 . 基于改进强化学习的机器人路径规划研究 [J ] . 制造业自动化 , 2023 , 45 ( 3 ): 148 - 151 , 172 .

LI W , ZHANG X D , JIANG X F , et al . Robot path planning research based on improved reinforcement learning [J ] . Manufacturing Automation , 2023 , 45 ( 3 ): 148 - 151 , 172 .

唐静远 , 魏文军 . 离散多智能体有限时间Q学习协同输出调节 [J ] . 计算机应用研究 , 2023 , 40 ( 1 ): 204 - 208 .

TANG J Y , WEI W J . Cooperative output regulation of finite time Q-learning for discrete multi-agents [J ] . Application Research of Computers , 2023 , 40 ( 1 ): 204 - 208 .

胡晓辉 . 一种基于动态参数调整的强化学习动作选择机制 [J ] . 计算机工程与应用 , 2008 , 44 ( 28 ): 29 - 31 , 48 .

HU X H . Action choice mechanism of reinforcement learning based on adjusted dynamic parameters [J ] . Computer Engineering and Applications , 2008 , 44 ( 28 ): 29 - 31 , 48 .

DINKELBACH W . On nonlinear fractional programming [J ] . Management Science , 1967 , 13 ( 7 ): 492 - 498 .

KHA H H , TUAN H D , NGUYEN H H . Fast global optimal power allocation in wireless networks by local D.C.programming [J ] . IEEE Transactions on Wireless Communications , 2012 , 11 ( 2 ): 510 - 515 .

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