An autonomic joint radio resource management algorithm in end-to-end reconfigurable system  

作　　者：林粤伟 

机构地区：[1]Key Laboratory of Universal Wireless Communications,Ministry of Education,Beijing University of Posts and Telecommunications,Beijing 100876,P.R.China

出　　处：《High Technology Letters》2008年第3期238-244,共7页高技术通讯（英文版）

基　　金：the National Natural Science Foundation of China(No.60632030);the National High Technology Research and Development Program of China(No.2006AA01Z276)

摘　　要：This paper presents the multi-step Q-learning(MQL)algorithm as an autonomic approach to thejoint radio resource management(JRRM)among heterogeneous radio access technologies(RATs)in theB3G environment.Through the'trial-and-error'on-line learning process,the JRRM controller can con-verge to the optimized admission control policy.The JRRM controller learns to give the best allocation foreach session in terms of both the access RAT and the service bandwidth.Simulation results show that theproposed algorithm realizes the autonomy of JRRM and achieves well trade-off between the spectrum utilityand the blocking probability comparing to the load-balancing algorithm and the utility-maximizing algo-rithm.Besides,the proposed algorithm has better online performances and convergence speed than theone-step Q-learning(QL)algorithm.Therefore,the user statisfaction degree could be improved also.This paper presents the multi-step Q-learning （MQL） algorithm as an autonomic approach to the joint radio resource management （JRRM） among heterogeneous radio access technologies （RATs） in the B3G environment. Through the ＂trial-and-error＂ on-line learning process, the JRRM controller can converge to the optimized admission control policy. The JRRM controller learns to give the best allocation for each session in terms of both the access RAT and the service bandwidth. Simulation results show that the proposed algorithm realizes the autonomy of JRRM and achieves well trade-off between the spectrum utility and the blocking probability comparing to the load-balancing algorithm and the utility-maximizing algorithm. Besides, the proposed algorithm has better online performances and convergence speed than the one-step Q-learning （QL） algorithm. Therefore, the user statisfaction degree could be improved also.

关 键 词：joint radio resource management reinforcement learning AUTONOMIC end-to-end reconfigurability heterogeneous networks 

分 类 号：TN92[电子电信—通信与信息系统]