Energy-efficient resource allocation for device-to-device communication with WPT

被引:30
|
作者
Dai, Haibo
Huang, Yongming
Li, Chunguo
Li, Shidang
Yang, Luxi [1 ]
机构
[1] Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China
基金
国家高技术研究发展计划(863计划); 中国国家自然科学基金;
关键词
cellular radio; resource allocation; radiofrequency power transmission; energy conservation; game theory; learning (artificial intelligence); optimisation; robust distributed learning algorithm; optimisation problem; Nash equilibrium; gametheoretic learning approach; cellular network; wireless power transfer technique; WPT; device-to-device communication; energy-efficient downlink resource allocation problem; POWER ALLOCATION; CELLULAR NETWORKS; GAME; SELECTION; DESIGN;
D O I
10.1049/iet-com.2015.1192
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this study, the authors address the downlink resource (subchannels and power) allocation problem for device-to-device communication with wireless power transfer technique in a cellular network to improve the energy efficiency (EE). The considered problem is formulated as maximising the weighted EE and is solved by leveraging a game-theoretic learning approach. Specifically, they first prove that an exact potential game applies to the resource allocation problem and there exists the best Nash equilibrium (NE) which is the optimal solution of the optimisation problem. Then, aiming to this optimisation problem with imperfect information, a robust and distributed learning algorithm is proposed and is proved that it can converge to the best NE. Finally, numerical results verify the effectiveness of the proposed scheme.
引用
收藏
页码:326 / 334
页数:9
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