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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). Furthermore, we present a novel Bayesian network based...
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 algorithm shows that the proposed approach exhibits...
This paper proposes the application of a quantum inspired reinforcement learning technique for spectrum assignment of wireless communication networks. The proposed technique aims to enhance the speed of learning convergence through the dependence of the decision process on a well ranked action desirability table which is updated based on the success or failure of an action. In addition, the exploration...
This paper examines how flexible cellular system architectures and efficient spectrum management techniques can be used to play a key role in accommodating the exponentially increasing demand for mobile data capacity in the near future. The efficiency of the use of radio spectrum for wireless communications can be dramatically increased by dynamic secondary spectrum sharing; an intelligent approach...
This paper investigates dynamic topology management functionalities in the flexible deployment of a hybrid aerial-terrestrial cognitive cellular network for public safety in unexpected or temporary events. An evolutionary roll out and roll back of the network architecture in the disaster relief scenario is proposed under the assistant of topology management algorithm, in order to manage the number,...
This paper considers designing efficient medium access strategies for secondary users (SUs) to select frequency channels to sense and access in cognitive radio networks. The interaction among the SUs is considered as a learning problem, in which every SU behaves as an intelligent agent. Each SU believes that its competitors alter their future medium access strategies in proportion to its own current...
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