Simultaneity in discrete-time single server queues with Bernoulli inputs
Performance Evaluation
Discrete-Time GeoX/G/1 Queue with Preemptive Repeat Different Priority
Queueing Systems: Theory and Applications
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
The Analysis of the Optimal Contention Period for Broadband Wireless Access Network
PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
Markov chain analysis of uplink subframe in polling-based WiMAX networks
Computer Communications
A performance study of uplink scheduling algorithms in point-to-multipoint WiMAX networks
Computer Communications
An efficient analytical model for WiMAX networks with multiple traffic profiles
Mobility '08 Proceedings of the International Conference on Mobile Technology, Applications, and Systems
A survey of MAC based QoS implementations for WiMAX networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Wireless Personal Communications: An International Journal
Queueing analysis of polled service classes in the IEEE 802.16 MAC protocol
IEEE Transactions on Wireless Communications
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This paper analyses the performance and optimisation of uplink traffic in IEEE 802.16 networks of the different service classes. IEEE 802.16 standard suite is currently the latest broadband wireless access reservation-based bandwidth allocation mechanism. An 802.16 wireless service provides a communications path between a subscriber station SS and a base station BS with uplink and downlink directions. A SS has to be polled to request bandwidth reservation before transmits uplink data to a BS with an appropriate QoS. A discrete-time Geo/G/1 queuing model is utilised to investigate the performance of uplink traffic in IEEE 802.16 networks using the imbedded Markov chain technique. Some performance measures and illustrative numerical results have also been discussed. A cost model is developed to determine the optimal values of arrival and service rate at a minimum cost. The genetic algorithm is employed to search the optimal values of arrival and service parameters for the system.