Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Fundamentals of WiMAX: Understanding Broadband Wireless Networking (Prentice Hall Communications Engineering and Emerging Technologies Series)
High performance publish/subscribe middleware for mobile wireless networks
Mobile Information Systems
Mobile Information Systems
An Efficient Analytical Model for the Dimensioning of WiMAX Networks
NETWORKING '09 Proceedings of the 8th International IFIP-TC 6 Networking Conference
WiMAX Evolution: Emerging Technologies and Applications
WiMAX Evolution: Emerging Technologies and Applications
WD'09 Proceedings of the 2nd IFIP conference on Wireless days
Journal of Network and Computer Applications
IEEE Transactions on Mobile Computing
ISCC '11 Proceedings of the 2011 IEEE Symposium on Computers and Communications
Fast Reinforcement Learning for Energy-Efficient Wireless Communication
IEEE Transactions on Signal Processing
Improving throughput in WiMAX communication at vehicular speeds
Journal of High Speed Networks
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WiMAX Worldwide Interoperability for Microwave Access constitutes a candidate networking technology towards the 4G vision realization. By adopting the Orthogonal Frequency Division Multiple Access OFDMA technique, the latest IEEE 802.16x amendments manage to provide QoS-aware access services with full mobility support. A number of interesting scheduling and mapping schemes have been proposed in research literature. However, they neglect a considerable asset of the OFDMA-based wireless systems: the dynamic adjustment of the downlink-to-uplink width ratio. In order to fully exploit the supported mobile WiMAX features, we design, develop, and evaluate a rigorous adaptive model, which inherits its main aspects from the reinforcement learning field. The model proposed endeavours to efficiently determine the downlink-to-uplink width ratio, on a frame-by-frame basis, taking into account both the downlink and uplink traffic in the Base Station BS. Extensive evaluation results indicate that the model proposed succeeds in providing quite accurate estimations, keeping the average error rate below 15% with respect to the optimal sub-frame configurations. Additionally, it presents improved performance compared to other learning methods e.g., learning automata and notable improvements compared to static schemes that maintain a fixed predefined ratio in terms of service ratio and resource utilization.