动态频谱接入的Q学习优化算法

doi:10.3969/j.issn.1001-2400.2015.06.030

J4 ›› 2015, Vol. 42 ›› Issue (6): 179-183.doi: 10.3969/j.issn.1001-2400.2015.06.030

动态频谱接入的Q学习优化算法

黄影1;严定宇2;李男2

(1. 西安文理学院数学与计算机工程学院，陕西西安 710068;
2. 西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710071)

收稿日期:2014-07-16 出版日期:2015-12-20 发布日期:2016-01-25
通讯作者: 黄影
作者简介:黄影(1977-)，女，助理工程师，硕士，E-mail：yhuang@xidian.edu.cn．
基金资助:
国家自然科学基金资助项目(61172068，61373170)；中央高校基本科研业务费专项资金资助项目(JB150317)

Optimization algorithm for dynamic spectrum access based on Q-learning in cognitive radio networks

HUANG Ying1;YAN Dingyu2;LI Nan2

(1. School of Mathematics and Computer Engineering, Xi'an Univ. of Arts and Science, Xi'an 710068, China;
2. State Key Lab. of Integrated Service Networks, Xidian Univ., Xi'an 710071, China)

Received:2014-07-16 Online:2015-12-20 Published:2016-01-25
Contact: HUANG Ying

摘要/Abstract

摘要：

在认知无线电网络的中心架构下针对网络整体性能和用户需求，提出一种基于Q学习的动态频谱接入优化算法．该算法通过认知用户根据Q学习算法提出信道申请和控制节点；根据网络整体性能处理申请两个主要步骤的实施，完成动态频谱的接入．仿真与分析表明，该优化算法在满足用户频谱需求的同时使得网络整体性能得到明显的改善．

关键词: 认知无线电网络, 动态频谱接入, Q学习

Abstract:

Under the centralized cognitive radio network architecture, considering the network performance and users' demands. We propose an optimized dynamic spectrum access algorithm based on Q-learning. The proposed algorithm has two steps, which consist of user request according to Q-learning and the application process according to the overall system performance. Simulation results show that the proposed scheme can improve the overall system performance obviously, and that the user requirements could be satisfied at the same time.

Key words: cognitive radio networks, dynamic spectrum access, Q-learning

黄影;严定宇;李男. 动态频谱接入的Q学习优化算法[J]. J4, 2015, 42(6): 179-183.

HUANG Ying;YAN Dingyu;LI Nan. Optimization algorithm for dynamic spectrum access based on Q-learning in cognitive radio networks[J]. J4, 2015, 42(6): 179-183.

参考文献

［1］ Marinho J, Monteiro E. Cognitive Radio: Survey on Communication Protocols, Spectrum Decision Issues, and Future Research Directions［J］. Wireless Networks, 2012, 18(2): 147-164.
［2］ Mitola J, Maguire Jr G Q. Cognitive Radio: Making Software Radios More Personal［J］. IEEE Personal Communications, 1999, 6(4): 13-18.
［3］ Tragos E Z, Zeadally S, Fragkiadakis A G, et al. Spectrum Assignment in Cognitive Radio Networks: a Comprehensive Survey［J］. IEEE Communications Surveys and Tutorials, 2013, 15(3): 1108-1135.
［4］ Gavrilovska L, Atanasovski V, Macaluso I, et al. Learning and Reasoning in Cognitive Radio Networks［J］. IEEE Communications Surveys ＆ Tutorials, 2013, 15(4): 1761-1777.
［5］ Xu Y, Wang J, Wu Q, et al. Opportunistic Spectrum Access in Unknown Dynamic Environment: a Game-theoretic Stochastic Learning Solution［J］. IEEE Wireless Communications, 2012, 11(4): 1380-1391.
［6］ Chen Z, Qiu R C. Q-learning Based Bidding Algorithm for Spectrum Auction in Cognitive Radio［C］//Proceedings of IEEE Southeastcon. Piscataway: IEEE, 2011: 409-412.
［7］ Liu C B, Jiang H, Yang Y C, et al. Q-learning-based Cross-layer Learning Engine Design for Cognitive Radio Network［C］//Proceedings of SPIE: 8784. Bellingham: SPIE, 2013: 878419.
［8］ Peng J, Li J, Li S, et al. Multi-relay Cooperative Mechanism with Q-learning in Cognitive Radio Multimedia Sensor Networks［C］//Proceedings of the IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications. Piscataway: IEEE, 2011: 1624-1629.
［9］ Bkassiny M, Li Y, Jayaweera S K. A Survey on Machine-learning Techniques in Cognitive Radios［J］. IEEE Communications Surveys ＆ Tutorials, 2013, 15(3): 1136-1159.
［10］ Rummery G A, Niranjan M. On-line Q-learning Using Connectionist Systems［M］. Cambridge: University of Cambridge, 1994.

[1]	肖林;刘凯. 认知无线电网络中的最佳可信度频谱检测算法[J]. J4, 2011, 38(5): 79-84.
[2]	郭彩丽;曾志民;冯春燕;刘芳芳. 一种认知无线电网络中的新型主动频谱选择算法 [J]. J4, 2008, 35(6): 1121-1126.

动态频谱接入的Q学习优化算法

Optimization algorithm for dynamic spectrum access based on Q-learning in cognitive radio networks

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

Metrics

本文评价

推荐阅读 10