Q-Learning Based Sensing Task Management Algorithm for Cognitive Radio Systems

Li Mo; Xu You-yun; Cai Yue-ming

doi:10.3724/SP.J.1146.2009.00296

Volume 32 Issue 3

Aug. 2010

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2010 > 32(3): 623-628

Li Mo, Xu You-yun, Cai Yue-ming. Q-Learning Based Sensing Task Management Algorithm for Cognitive Radio Systems[J]. Journal of Electronics & Information Technology, 2010, 32(3): 623-628. doi: 10.3724/SP.J.1146.2009.00296

Citation:

Li Mo, Xu You-yun, Cai Yue-ming. Q-Learning Based Sensing Task Management Algorithm for Cognitive Radio Systems[J]. Journal of Electronics & Information Technology, 2010, 32(3): 623-628. doi: 10.3724/SP.J.1146.2009.00296

Citation:

PDF( 305 KB)

Q-Learning Based Sensing Task Management Algorithm for Cognitive Radio Systems

doi: 10.3724/SP.J.1146.2009.00296

Received Date: 2009-03-09
Rev Recd Date: 2009-09-21
Publish Date: 2010-03-19

Abstract

Abstract

More than an adaptive system, the cognitive radio system is an intelligent system. The Q-Learning of the intelligent control theory is adopted in the paper, to solve the sensing task allocation problem among cognitive users. And a Q-Learning based sensing management algorithm is proposed. The algorithm allocates sensing tasks to users through times of interaction with the environment and self-learning. The scheme of the paper works without any channel state information and estimation of primary traffic. From the simulation result, the algorithm could improve the sensing efficiency compared to the static allocation algorithm and attain to the convergence in a short time, which could be an attempt to the future intelligent cognitive radio systems.

FullText(HTML)

References(1)

References

Haykin S. Cognitive radio: brain-empowered wirelesscommunications[J].IEEE Journal on Selected Areas inCommunications.2005, 23(2):201-220[2]Hu Wen-dong, Willkomm D, and Vlantis G, et al.. Dynamicfrequency hopping communities for effieient IEEE 802.22operation[J]. IEEE Communication Magazine, 2007, 45(5):80-87.[3]Jeong Sang Soo, Jeon Wha Sook, and Jeong Dong Geun.Dynamic channel sensing management for OFDMA-basedcognitive radiosystems[C]. Proceeding of VTC 2007, Dublin,2007: 2646-2650.[4]Watkin C and Dayan P. Q-Learning [J]. Machine Learning,1992, 8(3): 279-292.[5]Nie Jun-hong and Haykin S. A Q-Learning-based dynamicchannel assignment technique for mobile communicationsystems[J].IEEE Transactions on Vehicular Technology.1999, 48(5):1676-1687[6]Chen Yih-Shen, Chang Chung-Ju, and Ren Fang-Chin.Q-Learning-based multirate transmission control scheme forRRM in multimedia WCDMA systems[J].IEEETransactions on Vehicular Technology.2004, 53(1):38-48[7]Chang Chung-ju, Chang Chia-yuan, and Ren Fang-ching.Q-Learning-based hybrid ARQ for high speed downlinkpacket access in UMTS[C]. Proceeding of VTC2007, Dublin,2007: 2610-2615.[8]Reddy Y B. Detecting primary signals for efficientutilization of spectrum using Q-Learning[C]. Proceeding ofthe Fifth International Conference on InformationTechnology: New Generations, Las Vegas, 2008: 360-365.[9]Chen Yih-shen, Chang Chung-ju, and Ren Fang-chin.Situation-aware data access manager using fuzzy Q-learningtechnique for multi-cell WCDMA systems[J].IEEETransactions on Wireless Communications.2006, 5(9):2539-2547[10]Nasri R.[J].Altman Z, and Dubreil H. Optimal tradeoffbetween RT and NRT services in 3G-CDMA networksusing dynamic fuzzy Q-Learning[C]. Proceeding ofPIMRC06, Helsinki.2006,:-[11]张永靖, 冯志勇, 张平. 基于Q 学习的自主联合无线资源管理算法[J].电子与信息学报.2008, 30(3):676-680浏览

Relative Articles

Supplements(0)

Cited By

Proportional views