Minimizing Transmission Costs through Adaptive Marking in Differentiated Services Networks
MMNS '02 Proceedings of the 5th IFIP/IEEE International Conference on Management of Multimedia Networks and Services: Management of Multimedia on the Internet
Short survey: Taxonomy and survey of RFID anti-collision protocols
Computer Communications
Assured end-to-end QoS through adaptive marking in multi-domain differentiated services networks
Computer Communications
An efficient MAC protocol for throughput enhancement in dense RFID system
ISWPC'09 Proceedings of the 4th international conference on Wireless pervasive computing
Spectrum self-coexistence in cognitive wireless access networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Learning-Based spectrum selection in cognitive radio ad hoc networks
WWIC'10 Proceedings of the 8th international conference on Wired/Wireless Internet Communications
Particle swarm intelligence for channel assignment problem in mobile cellular communication system
International Journal of Artificial Intelligence and Soft Computing
Optimization of load balancing using fuzzy Q-Learning for next generation wireless networks
Expert Systems with Applications: An International Journal
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One of the fundamental issues in the operation of a mobile communication system is the assignment of channels to cells and to calls. This paper presents a novel approach to solving the dynamic channel assignment (DCA) problem by using a form of real-time reinforcement learning known as Q-learning in conjunction with neural network representation. Instead of relying on a known teacher the system is designed to learn an optimal channel assignment policy by directly interacting with the mobile communication environment. The performance of the Q-learning based DCA was examined by extensive simulation studies on a 49-cell mobile communication system under various conditions. Comparative studies with the fixed channel assignment (FCA) scheme and one of the best dynamic channel assignment strategies, MAXAVAIL, have revealed that the proposed approach is able to perform better than the FCA in various situations and capable of achieving a performance similar to that achieved by the MAXAVAIL, but with a significantly reduced computational complexity