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—This paper presents the concept of the Win-or-Learn-Fast (WoLF) variable learning rate for distributed Q-learning 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(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 a distributed Q-learning based dynamic spectrum access (DSA) algorithm is applied to a cognitive cellular system designed for providing ultra high capacity density with only secondary access to an LTE channel. Large scale simulations of a stadium temporary event scenario show that the distributed Q-learning based DSA scheme provides robust(More)