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文章摘要
一种面向电力物联网的认知D2D网络能效资源分配算法
Energy-efficient resource allocation algorithm for cognitive D2D networks for power IoT
Received:October 20, 2020  Revised:November 20, 2020
DOI:10.19753/j.issn1001-1390.2023.02.014
中文关键词: D2D通信  能效  资源分配  鲁棒性
英文关键词: D2D communication, energy efficiency, resource allocation, robustness
基金项目:国家自然科学基金资助项目(61971190);中央高校基本科研业务费专项资金资助(2019MS089);中央高校项目(2017MS113)
Author NameAffiliationE-mail
Huang Xing State Grid Liaoning Electric Power Co,Ltd Shenyang huangxing@139.com 
Zhang Wenjie State Grid Liaoning Electric Power Co,Ltd Shenyang zhangwenjie@163.com 
Li Xi State Grid Liaoning Electric Power Co,Ltd Shenyang lixi123@ln.sgcc.com.cn 
Zhang Yu State Grid Liaoning Electric Power Co,Ltd Shenyang zhangyu111@163.com 
Li Baogang North China Electric Power University Baoding baogangli@163.com 
Jia Huibin* North China Electric Power University Baoding huibin.jia@foxmail.com 
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中文摘要:
      在电力物联网中为了提高D2D(Device-to-Device)网络的频谱效率和系统能效,考虑不完美信道状态信息(Channel State Information, CSI)的影响,提出了一种鲁棒能效最大的资源分配算法。考虑D2D用户最大发射功率约束、蜂窝用户功率约束和共道干扰功率约束,建立下垫式频谱共享模式下混合蜂窝/D2D通信的上行传输资源分配模型;考虑蜂窝用户干扰中断约束,基于最小最大概率机和辅助变量法,将原非凸优化问题转换为凸优化求解。基于凸优化理论,获得鲁棒资源分配问题的解析解。仿真结果表明所提算法可以提升2%左右的能效性能。
英文摘要:
      In order to improve spectral efficiency and system energy efficiency of device-to-device (D2D) networks in power IoT, a robust energy-efficient maximization resource allocation algorithm is proposed under the consideration of the influence of imperfect channel state information (CSI). Considering the maximum transmit power constraint of D2D user, the cellular user power constraint and the co-channel interference power constraint, an uplink transmission resource allocation model is formulated for hybrid cellular/D2D communications with underlay frequency spectrum sharing. Based on the outage interference constraint of cellular user, the formulated non-convex optimization problem is converted into a convex one by using the mini-max probability machine and the auxiliary variable method. The analytical solutions of robust resource allocation problem are obtained by convex optimization theory. Simulation results show the proposed algorithm can improve system energy efficiency by 2%.
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