南京大学学报(自然科学版) ›› 2019, Vol. 55 ›› Issue (2): 202–210.doi: 10.13232/j.cnki.jnju.2019.02.005

• • 上一篇    下一篇

协作中继传输网络中基于无线供电的能量交易方法

彭 宏1,刘 晨1,卢为党1*,张 昱1,刘 鑫2,徐志江1   

  1. 1.浙江工业大学信息工程学院,杭州,310023;2.大连理工大学信息与通信工程学院,大连,116024
  • 接受日期:2018-10-20 出版日期:2019-04-01 发布日期:2019-03-31
  • 通讯作者: 卢为党 E-mail:luweid@zjut.edu.cn
  • 基金资助:
    国家自然科学基金(61871348),中国博士后科学基金(2017M612027)

Energy trading method based on wireless power supply in cooperative transmission network

Peng Hong1,Liu Chen1,Lu Weidang1*,Zhang Yu1,Liu Xin2,Xu Zhijiang1   

  1. 1.College of Information Engineering,Zhejiang University of Technology,Hangzhou,310023,China; 2.School of Inforamtion and Communication Engineering,Dalian University of Technology,Dalian,116024,China
  • Accepted:2018-10-20 Online:2019-04-01 Published:2019-03-31
  • Contact: Lu Weidang E-mail:luweid@zjut.edu.cn

摘要: 为了让协作中继传输网络中的中继节点能够获得能量自愿协作转发源节点的信息,提高信息的传输效率,提出一种基于无线供电的能量交易方法. 所提方法中包含一个源节点和目的节点以及一个中继节点和多个能源供应点(Energy Supply Points,ESP). 当源节点到目的节点链路的信道质量较差时,需要中继节点帮助转发源节点的信息到目的节点. 但是中继节点往往是自私的,不愿意牺牲自己的能量帮助转发别人的信息. 为了使得中继节点愿意帮助转发源节点的信息,目的节点支付给周围ESP一定的报酬,以换取其通过无线供电的方式给中继节点提供能量,使得中继节点能够利用接收到的能量帮助转发源节点的信息. 通过利用斯塔克尔伯格模型,研究如何对所提方法中目的节点支付给ESP的报酬和ESP提供给中继节点的能量进行优化,最大化目的节点和ESP双方的效用. 仿真结果表明,本文所提的能量交易方法能够让目的节点和ESP的效用同时达到最优值,有效提高信息的传输效率.

关键词: 能量交易, 无线供电, 斯塔克尔伯格理论, 能源价格, 效用函数

Abstract: To improve the information transmission efficiency in cooperative transmission network,we propose an energy trading method based on wireless power supply,which enables the relay node obtain the power to voluntarily help forward the information of source node. In the proposed method,the system consists of a source node,a relay node,a destination node and several energy supply points(ESP). When the channel quality between the source-to-destination link is poor,the source node will need the relay node to help forward its information to the destination node. However,the relay node will always be selfish,which may be unwilling to sacrifice its power to help forward the other node's information. In order to make the relay node voluntarily help forward the information of the source node,the destination node pays a certain reward to the surrounding ESP in exchange for the ESP providing energy to the relay node through wireless power supply,which enables the relay node use the received energy to help forward the information of the source node. By utilizing Stackelberg model,we study how to optimize the reward paid by the destination node to the ESP and the power supplied to the relay node from the ESP,such that the utility of both destination node and ESP are maximized. It can be demonstrated from the simulation results that the proposed energy trading method can make the utility of destination node and ESP achieve the maximum value simultaneously,which can effectively improve the information transmission efficiency.

Key words: energy trading, wireless power supply, Stackelberg theory, energy price, utility function

中图分类号: 

  • TN925
[1] Laneman J N,Tse D N C,Wornell G W. Cooperative diversity in wireless networks:Efficient protocols and outage behavior. IEEE Transac-tions on Information Theory,2004,50(12):3062-3080.
[2] Bletsas A,Khisti A,Reed D P,et al. A simple cooperative diversity method based on network path selection. IEEE Journal on Selected Areas in Communications,2006,24(3):659-672.
[3] Bi S Z,Ho C K,Zhang R. Wireless powered communication:Opportunities and challenges. IEEE Communications Magazine,2015,53(4):117-125.
[4] Huang K B,Zhou X Y. Cutting the last wires for mobile communications by microwave power transfer. IEEE Communications Magazine,2015,53(6):86-93.
[5] Lu X,Wang P,Niyato D,et al. Wireless networks with RF energy harvesting:A contemporary survey. IEEE Communications Surveys & Tutorials,2015,17(2):757-789.
[6] Niyato D,Kim D I,Wang P,et al. A novel caching mechanism for Internet of Things(IoT)sensing service with energy harvesting ∥ IEEE International Conference on Communications. Kuala Lumpur,Malaysia:IEEE Press,2016:1-6.
[7] Visser H J,Vullers R J M. RF energy harvesting and transport for wireless sensor network applications:Principles and requirements. Proceedings of the IEEE,2013,101(6):1410-1423.
[8] Chen H,Li Y H,Rebelatto J L,et al. Harvest-then-cooperate:Wireless-powered cooperative communications. IEEE Transactions on Signal Processing,2015,63(7):1700-1711.
[9] Ju H,Zhang R. User cooperation in wireless powered communication networks ∥ IEEE Global Communications Conference. Austin,TX,USA:IEEE,2015:1430-1435.
[10] Ju H,Zhang R. Throughput maximization in wireless powered communication networks. IEEE Transactions on Wireless Communications,2014,13(1):418-428.
[11] Krikidis I,Timotheou S,Sasaki S. RF energy transfer for cooperative networks:Data relaying or energy harvesting?IEEE Communications Letters,2012,16(11):1772-1775.
[12] Zeng Y,Zhang R. Full-duplex wireless-powered relay with self-energy recycling. IEEE Wireless Communications Letters,2015,4(2):201-204.
[13] Lu W D,Gong Y,Wu J Y,et al. Simultaneous wireless information and power transfer based on joint subcarrier and power allocation in OFDM systems. IEEE Access,2017,5:2763-2770.
[14] Lu W D,Fang S Z,Gong Y,et al. Resource allocation for OFDM relaying wireless power transfer based energy-constrained UAV communication network ∥ IEEE International Conference on Communications Workshops. Kansas City,KS,USA:IEEE Press,2018:1-6.
[15] Huang C,Zhang R,Cui S G. Throughput maximization for the Gaussian relay channel with energy harvesting constraints. IEEE Journal on Selected Areas in Communications,2013,31(8):1469-1479.
[16] Medepally B,Mehta N B. Voluntary energy harvesting relays and selection in cooperative wireless networks. IEEE Transactions on Wireless Communications,2010,9(11):3543-3553.
  [17] Zhang Y R,Pan M,Song L Y,et al. A survey of contract theory-based incentive mechanism design in wireless networks. IEEE Wireless Communications,2017,24(3):80-85.
[18] Gao L,Wang X B,Xu Y Y,et al. Spectrum trading in cognitive radio networks:A contract-theoretic modeling approach. IEEE Journal on Selected Areas in Communications,2011,29(4):843-855.
[19] Wang H B,Gao L,Gan X Y,et al. Cooperative spectrum sharing in cognitive radio networks:A game-theoretic approach ∥ IEEE International Conference on Communications. Cape Town,South Africa:IEEE Press,2010:23-27.
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 范 君, 业巧林, 业 宁. 基于线性鉴别的无参数局部保持投影算法[J]. 南京大学学报(自然科学版), 2019, 55(2): 211 -220 .
[2] 王广辉,杨高东,周 政,张志炳. 丙酸异龙脑酯的合成、反应热力学和反应动力学研究[J]. 南京大学学报(自然科学版), 2019, 55(3): 486 -497 .
[3] 徐扬,周文瑄,阮慧彬,孙雨,洪宇. 基于层次化表示的隐式篇章关系识别[J]. 南京大学学报(自然科学版), 2019, 55(6): 1000 -1009 .
[4] 刘作国,陈笑蓉. 汉语句法分析中的论元关系模型研究[J]. 南京大学学报(自然科学版), 2019, 55(6): 1010 -1019 .
[5] 段友祥,柳璠,孙歧峰,李洪强. 基于相带划分的孔隙度预测[J]. 南京大学学报(自然科学版), 2019, 55(6): 934 -941 .
[6] 游杰, 胡广, 张玺华, 沈安江, 彭瀚霖, 田兴旺, 赵东方. 微生物碳酸盐岩同生⁃早成岩阶段有机质降解示踪:以四川盆地灯影组四段为例[J]. 南京大学学报(自然科学版), 2020, 56(3): 308 -321 .
[7] 戴海亮,沈斌,李开开,张小涛,徐学敏,许智超,周晶晶. 地质条件约束下川北二叠系大隆组富有机质页岩热模拟生烃过程及特征研究[J]. 南京大学学报(自然科学版), 2020, 56(3): 382 -392 .
[8] 陈俊芬,赵佳成,韩洁,翟俊海. 基于深度特征表示的Softmax聚类算法[J]. 南京大学学报(自然科学版), 2020, 56(4): 533 -540 .