Bi Yu-ting
Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Computer School, Beijing Information Science and Technology University, 100101, Beijing, China
Chen Xin
Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Computer School, Beijing Information Science and Technology University, 100101, Beijing, China
Wang Hong-lu
Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Computer School, Beijing Information Science and Technology University, 100101, Beijing, China
Liu Zong-Qi
Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Computer School, Beijing Information Science and Technology University, 100101, Beijing, China
ABSTRACT
An efficient spectrum allocation strategy is crucial to improving the spectrum utilization and Quality of Service (QoS) of users in femtocell networks. In this study, we formulate the downlink channel allocation problem in femtocell networks into a dynamic optimization problem. The formulation captures the stochastic nature of external packet arrivals as well as channel availability. Then, based on Q-learning techniques, we propose an Adaptive Spectrum Allocation Algorithm (ASAA), with a ε-greedy action exploration policy for the purposing of accelerating the rate of convergence. The algorithm can learn the statistics of both packet arriving processes and channel availability. In addition, it produces a near-optimal spectrum allocation strategy that aims at maximizing the system throughput. Simulation results demonstrate that the algorithm converges to a stationary spectrum allocation policy, which outperforms the classic Round Robin (RR) allocation policy.
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How to cite this article
Bi Yu-ting, Chen Xin, Wang Hong-lu and Liu Zong-Qi, 2013. An Adaptive Spectrum Allocation Algorithm in Femtocell Networks Using Q-learning. Information Technology Journal, 12: 7353-7360.
DOI: 10.3923/itj.2013.7353.7360
URL: https://scialert.net/abstract/?doi=itj.2013.7353.7360
DOI: 10.3923/itj.2013.7353.7360
URL: https://scialert.net/abstract/?doi=itj.2013.7353.7360
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