User association mechanism based on evolutionary game

doi:10.11772/j.issn.1001-9081.2019112024

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (5): 1392-1396.DOI: 10.11772/j.issn.1001-9081.2019112024

• Network and communications • Previous Articles Next Articles

User association mechanism based on evolutionary game

WANG Yueping¹, XU Tao²

1.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, NanjingJiangsu 211106, China
2.School of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China

Received:2019-11-04 Revised:2020-01-13 Online:2020-05-15 Published:2020-05-10
Contact: WANG Yueping, born in 1971, Ph. D. candidate. His research interests include wireless networking.
About author:WANG Yueping, born in 1971, Ph. D. candidate. His research interests include wireless networking.XU Tao, born in 1965, Ph. D., professor. His research interests include computer network.

基于演化博弈的用户接入机制

王月平¹, 徐涛²

1.南京航空航天大学计算机科学与技术学院，南京 211106
2.中国民航大学计算机科学与技术学院，天津 300300

通讯作者: 王月平(1971—)
作者简介:王月平(1971—)，男，北京人，博士研究生，主要研究方向：无线网络；徐涛（1965—），男，天津人，教授，博士， CCF会员，主要研究方向：计算机网络。

Abstract

Abstract:

User association is the problem that a wireless terminal chooses to access one serving base station. User association can be seen as the first step in wireless resource management, which has an important impact on network performance, and plays a very important role in achieving load balance, interference control, improvement of spectrum and energy efficiency. Aiming at the characteristics of multi-layer heterogeneous network including macro base stations and full-duplex small base stations, a separate multiple access mechanism was considered, which means allowing a terminal access different and multiple base stations in the uplink and downlink, so as to realize the performance improvement. On this basis, the user association problem with separation of uplink and downlink multi-access in heterogeneous network was modeled into an evolutionary game problem, in which the users act as the players to perform the resource competition with each other, the access choices of base stations are strategies in the game, and every user wants to obtain the maximum of own effectiveness by the choice of strategy. Besides, a low-complex self-organized user association algorithm was designed based on evolutionary game and reinforcement learning. In the algorithm, the user was able to adjust the strategy according to the revenue of current strategy choice, and reached an equilibrium state in the end, realizing user fairness. Finally, extensive simulations were performed to verify the effectiveness of the proposed method.

Key words: user association, heterogeneous network, separation of uplink and downlink, evolutionary game

摘要：

用户接入问题就是无线终端选择接入某个服务基站的问题。用户接入可以看作无线资源管理的第一步，对于网络性能有着重要的影响，在实现负载均衡、控制干扰、提高频谱和能量效率等方面起着非常重要的作用。针对包含宏基站和全双工小基站的多层异构网络特点，考虑了分离多接入机制，即允许一个终端在上行和下行接入到不同的多个基站，从而实现性能的提升。在此基础上，将异构网络中的上行下行分离多接入的用户接入问题建模成一个演化博弈问题，其中用户是博弈方，相互之间进行资源的竞争，基站的接入选择就是博弈中的策略，每个用户希望能通过策略的选择实现自身效用的最大化。此外，基于演化博弈和强化学习设计了低复杂度自组织用户接入算法，用户可以根据当前的策略选择收益来进行策略调整，并最终达到均衡状态，实现了用户公平性。最后通过大量的仿真实验验证了所提方法的有效性。

关键词: 用户接入, 异构网络, 上行下行分离, 演化博弈

CLC Number:

TP393
TN92

WANG Yueping, XU Tao. User association mechanism based on evolutionary game[J]. Journal of Computer Applications, 2020, 40(5): 1392-1396.

王月平, 徐涛. 基于演化博弈的用户接入机制[J]. 计算机应用, 2020, 40(5): 1392-1396.

References

1 LIU D , WANG L , CHEN Y , et al . User association in 5G networks: a survey and an outlook[J]. IEEE Communications Surveys and Tutorials, 2017, 18(2):1018-1044.
2 ANDREWS J G , BUZZI S , CHOI W , et al . What will 5G be?[J]. IEEE Journal on Selected Areas in Communications, 2014, 32(6): 1065-1082.
3 ZHANG T , ZHU Q . Game-based TDMA MAC protocol for vehicular network[J]. Journal of Communications and Networks, 2017, 19(3):209-217.
4 ZHANG Y , YANG C , LI J , et al . Distributed interference-aware traffic offloading and power control in ultra-dense networks: mean field game with dominating player[J]. IEEE Transactions on Vehicular Technology, 2019, 68(9): 8814-8826.
5 SEO J B, KWON T , CHOI J . Evolutionary game approach to uplink NOMA random access systems[J]. IEEE Communications Letters, 2019, 23(5): 930-933.
6 JIE Y, KAMAL A E , ALNUEN M A . Cooperative multi-path routing solution with real-time optimization for streaming application using auction theory[J]. IEEE Access, 2019, 7: 112520-112534.
7 王俊社,蒋彤彤,张琛,等 . 密集小蜂窝网络中基于用户接入的能效优化研究[J]. 计算机工程, 2019, 45(12):45-51,70. WANG J S , JIANG T T , ZHANG C , et al . Research on energy efficiency optimization based on user access in dense small cell network[J]. Computer Engineering, 2019, 45(12):45-51,70.
8 徐大专,邓昭 . 多用户接入与SLT编码的二维喷泉系统及性能分析[J]. 数据采集与处理, 2018, 33(6):945-952. (XU D Z, DENG Z. Two-dimensional multiuser access and SLT coding fountain system and its performance[J]. Journal of Data Acquisition and Processing, 2018, 33(6):945-952.)
9 HOSSAIN E , HASSAN M . 5G cellular: key enabling technologies and research challenges[J]. IEEE Instrumentation and Measurement Magazine, 2015, 18(3): 11-21.
10 WANG X , HAIDER F , GAO X , et al . Cellular architecture and key technologies for 5G wireless communication networks[J]. IEEE Communications Magazine, 2014, 52(2):122-130.
11 BOCCARDI F , HEATH R W , LOZANO A , et al . Five disruptive technology directions for 5G[J]. IEEE Communications Magazine, 2015, 52(2):74-80.
12 ELSHAER H , BOCCARDI, DOHLERH F . Downlink and uplink decoupling: a disruptive approach design for 5G networks[C]// Proceedings of the 2014 IEEE Global Communications Conference. Piscataway: IEEE, 2014: 1798-1803.
13 ZHENG K , YANG Z , ZHANG K , et al . Big data-driven optimization for mobile networks toward 5G[J]. IEEE Network, 2016, 30(1):44-51.
14 AHMAD S A , DATLA D . Distributed power allocation in heterogeneous networks with dual connectivity using backhaul state information[J]. IEEE Transactions on Wireless Communications, 2015, 14(8):4574-4581.
15 KAMEL M I , HAMOUDA W , YOUSSEF A M . Multiple association in ultra-dense networks[C]// Proceedings of the 2016 IEEE International Conference on Communications. Piscataway: IEEE, 2016: 1-6.

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User association mechanism based on evolutionary game

基于演化博弈的用户接入机制

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