A Novel Adaptive Call Admission Control Scheme for Distributed Reinforcement Learning Based Dynamic Spectrum Access in Cellular Networks
نویسندگان
چکیده
This paper introduces a novel Q-value based adaptive call admission control scheme (Q-CAC) for distributed reinforcement learning (RL) based dynamic spectrum access (DSA) in mobile cellular networks, which provides a good quality of service (QoS) without the need for spectrum sensing. A DSA algorithm has been developed in this paper using the stateless Q-learning algorithm with Win-or-Learn-Fast (WoLF) learning rates. Its performance was analysed using the spatial distribution of the probabilities of call blocking (BP) and dropping (DP) across the network and compared to that of a 100% accurate spectrum sensing based DSA scheme. The Q-CAC scheme demonstrated good controllability of the blocking probability using a Q-value based call admission threshold parameter. It significantly reduced spatial fluctuations in BP and DP, thus providing more cells with acceptable quality of service (QoS). Keywords—Dynamic Spectrum Access, Adaptive Call Admission Control, Distributed Reinforcement Learning
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