高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

面向分层异构网络的资源分配:一种稳健分层博弈学习方案

邵鸿翔 赵杭生 孙有铭 孙丰刚

邵鸿翔, 赵杭生, 孙有铭, 孙丰刚. 面向分层异构网络的资源分配:一种稳健分层博弈学习方案[J]. 电子与信息学报, 2017, 39(1): 38-44. doi: 10.11999/JEIT160285
引用本文: 邵鸿翔, 赵杭生, 孙有铭, 孙丰刚. 面向分层异构网络的资源分配:一种稳健分层博弈学习方案[J]. 电子与信息学报, 2017, 39(1): 38-44. doi: 10.11999/JEIT160285
SHAO Hongxiang, ZHAO Hangsheng, SUN Youming, SUN Fenggang. Resource Allocation for Heterogeneous Wireless Networks: A Robust Layered Game Learning Solutions[J]. Journal of Electronics & Information Technology, 2017, 39(1): 38-44. doi: 10.11999/JEIT160285
Citation: SHAO Hongxiang, ZHAO Hangsheng, SUN Youming, SUN Fenggang. Resource Allocation for Heterogeneous Wireless Networks: A Robust Layered Game Learning Solutions[J]. Journal of Electronics & Information Technology, 2017, 39(1): 38-44. doi: 10.11999/JEIT160285

面向分层异构网络的资源分配:一种稳健分层博弈学习方案

doi: 10.11999/JEIT160285
基金项目: 

国家自然科学基金(61471395, 61401508),江苏省自然科学基金(BK20161125)

Resource Allocation for Heterogeneous Wireless Networks: A Robust Layered Game Learning Solutions

Funds: 

The National Natural Science Foundation of China (61471395, 61401508), The Natural Science Foundation of Jiangsu Province, China (BK20161125)

  • 摘要: 该文研究了信道状态不确定条件下分层异构微蜂窝网络中的无线资源分配优化问题。首先引入信道不确定模型描述无线信道的随机动态性,并将该问题建模为考虑信道不确定度的双层鲁棒斯坦伯格博弈;然后给出了该博弈的均衡点分析;最后提出了一种分布式改进型分层Q学习方案以实现宏基站和微基站的均衡策略搜索。理论分析和仿真表明,所提出的分层博弈模型可以有效抑制由于信道状态不确定引起的收益下降。所采用的学习方案较传统Q学习方案收敛速度明显加快,更加适用于短时快变的通信环境。
  • ZAHIR T, ARSHAD K, NAKATA A, et al. Interference management in femtocells[J]. IEEE Communication Survey Tutorials, 2013, 15(1): 293-311. doi: 10.1109/SURV.2012. 020212.00101.
    HAN Zhu, NIYATO D, SAAD W, et al. Game Theory in Wireless and Communication Networks[M]. Cambridge: UK, Cambridge University Press, 2012: 88-91.
    扶奉超, 张志才, 路兆铭, 等. Femtocell双层网络中基于Stackelberg博弈的节能功率控制算法[J]. 电子科技大学学报, 2015, 44(3): 363-368.
    FU Fengchao, ZHANG Zhicai, LU Zhaoming, et al. Energy- efficient power control algorithm based on Stackelberg game in two-tier femtocell Networks[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(3): 363-368.
    LASHGARI M, MAHAM B, KEBRIAEI H, et al. Distributed power allocation and interference mitigation in two-tier femtocell networks: A game-theoretic approach[C]. Wireless Communications and Mobile Computing Conference, Dubrovnik, Croatia, 2015: 55-60.
    DUONG N D, MADHUKUMAR A S, and NIYATO D. Stackelberg Bayesian game for power allocation in two-tier networks[J]. IEEE Transactions on Vehicular Technology, 2016, 65(4): 2341-2354. doi: 10.1109/TVT.2015.2418297.
    ZHU Kun, HOSSAIN E, and ANPALAGAN A. Downlink power control in two-tier cellular OFDMA networks under uncertainties: A robust Stackelberg game[J]. IEEE Transactions on Communications, 2015, 63(2): 520-535. doi: 10.1109/TCOMM.2014.2382095.
    吴敏, 何勇. 鲁棒控制理论[M]. 北京: 高等教育出版社, 2010.
    ZHANG H, VENTURINO L, PRASAD N, et al. Weighted sum-rate maximization in multi-cell networks via coordinated scheduling and discrete power control[J]. IEEE Journal on Selected Areas in Communications, 2011, 29(6): 1214-1224. doi: 10.1109/JSAC.2011.110609.
    YANG K, WU Y, and HUANG J. Distributed robust optimization for communication networks[C]. IEEE Infocom Conference, Phoenix, AZ, USA, 2008: 1157-1165. doi: 10.1109/ INFOCOM.2008.171.
    FUDENBURG D and TIROLE J. Game Theory[M]. Cambridge, MA, USA, The MIT Press, 1991: 29-34.
    CHEN X, ZHANG H, CHEN T et al. Improving energy efficiency in femtocell networks: A hierarchical reinforcement learning framework[C]. IEEE International Conference on Communications (ICC), Budapest, Hungary, 2013: 2241- 2245. doi: 10. 1109/ICC.2013.6654861.
    WATKINS C and DAYAN P. Q-learning[J]. Journal of Machine Learning Research, 1992, 8(1): 279-292.
  • 加载中
计量
  • 文章访问数:  1322
  • HTML全文浏览量:  158
  • PDF下载量:  385
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-03-28
  • 修回日期:  2016-10-09
  • 刊出日期:  2017-01-19

目录

    /

    返回文章
    返回