A novel method for analysing the performance of power saving class of Type III in IEEE 802.16e is proposed, which is applicable to design, maintenance and management for mobile wireless metropolitan area network. Considering the memoryless nature of user initiated packet arrival, a Geom/G/1 queue model with multiple vacations and setup period is built to capture the principle for the power saving class of Type III. By using an embedded Markov chain method and the boundary state variable theory, we obtain the queueing measures such as queueing length, waiting time and busy cycle in steady state. Correspondingly, we derive explicitly the performance measures for the power saving class of Type III in terms of handover ratio, energy saving ratio, and average packet response time. Based on numerical results, we develop a cost function to determine numerically the optimal length of sleep window and the minimal cost with different offered loads.
IEEE 802.16e is currently the latest broadband wireless access standard designed to support mobility. In mobile networks, how to control energy consumption is one of the most important issues for battery-powered mobile stations. The standard proposes an energy saving mechanism named ＇sleep mode＇ for conserving the power of mobile stations. According to the operation principle of the sleep mode for downlink traffic in the type I power saving class, considering the self-similar nature of massive multimedia data in wireless networks, a discrete-time batch arrival Geom^x/G/1 queuing model with a close-down time and multiple vacations is built. The batch size is supposed to be Pareto distributed. By employing an embedded Markov chain method, the average queue length and the average sojourn time of the system model are derived. Correspondingly, the performance measures are obtained of the energy saving rate and the average packet delay time for the sleep mode in the IEEE 802.16e. The numerical results demonstrate the dependency relationships between the system performance measures and the system parameters with different offered loads and different self-similar degrees. Furthermore, a cost model is developed to determine the optimum length of the close-down time for minimizing the total system cost.
