Nils Morozs

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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(More)
This paper presents the concept of the Win-orLearn-Fast (WoLF) variable learning rate for distributed Qlearning based dynamic spectrum management algorithms. It demonstrates the importance of choosing the learning rate correctly by simulating a large scale stadium temporary event network. The results show that using the WoLF variable learning rate provides(More)
Case-based reinforcement learning is a combination of reinforcement learning (RL) and case-based reasoning which has been successfully applied to a variety of artificial intelligence problems concerned with dynamic environments. This paper demonstrates how case-based RL can be applied to distributed dynamic spectrum assignment in cellular networks with(More)
In this paper, we propose an algorithm for dynamic spectrum access (DSA) in LTE cellular systems-distributed ICIC accelerated Q-learning (DIAQ). It combines distributed reinforcement learning (RL) and standardized inter-cell interference coordination (ICIC) signalling in the LTE downlink, using the framework of heuristically accelerated RL (HARL).(More)
In this paper we propose a novel Bayesian network based model for analysing convergence properties of reinforcement learning (RL) based dynamic spectrum access (DSA) algorithms. It uses a minimum complexity DSA problem for probabilistic analysis of the joint policy transitions of RL algorithms. A Monte Carlo simulation of a distributed Q-learning DSA(More)