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| A Q-learning Based Autonomic Joint Radio Resource Management Algorithm |
| Zhang Yong-jing; Feng Zhi-yong; Zhang Ping |
| Telecommunication Engineering School, Beijing University of Posts and Telecommunications, Beijing 100876, China |
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Abstract A Q-learning based Joint Radio Resource Management (JRRM) algorithm is proposed for the autonomic resource optimization in a B3G system with heterogeneous Radio Access Technologies (RAT). Through the “trial-and-error” interactions with the radio environment, the JRRM controller learns to allocate the proper RAT and the service bandwidth for each session. A backpropagation neural network is adopted to generalize the large input state space to reduce memory requirement. Simulation results show that the proposed algorithm not only realizes the autonomy of JRRM through the online learning process, but also achieves well trade-off between the spectrum utility and the blocking probability.
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Received: 11 September 2006
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2008, Vol. 30 
