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

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

2.
(解放军理工大学通信工程学院南京 210007) ②(南京电讯技术研究所南京 210007) ③(解放军信息工程大学信息系统工程学院郑州 450000) ④(洛阳理工学院洛阳 471023)

基金项目:

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

计量
- 文章访问数: 1367
- HTML全文浏览量: 166
- PDF下载量: 386
- 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

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

2.
(College of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, China)

Funds:

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

摘要

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。
- 异构网络 /
- 斯坦伯格博弈 /
- 不完美信道信息 /
- 鲁棒决策 /
- 双层Q学习 /
- 离散策略
Abstract: This paper investigates a resource allocation scheme in heterogeneous wireless small cell networks with imperfect Channel State Information (CSI). In this work, the math expression for the stochastic dynamic uncertainty in CSI is proposed for model analysis and the robust Stackelberg game model with various interference power constraints is established firstly. Then, the Stackelberg game Equilibrium (SE) is obtained and analyzed. Lastly, an improved hierarchical Q-learning algorithm is also given to search the Stackelberg equilibrium strategies of macro-cell base station and small-cell base station. Both theoretical analysis and simulation results verify the proposed scheme can effectively restrain declining revenue due to incomplete CSI and the proposed algorithms can improves the convergence rate, especially applicable to the fast varying communication environment.
- Heterogeneous wireless networks /
- Stackelberg game /
- Incomplete Channel State Information (CSI) /
- Robust decision /
- Hierarchical Q-learning /
- Discrete strategy

HTML全文

参考文献(13)

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.

施引文献

资源附件(0)

访问统计