Adaptive Probabilistic Decision-Based Energy Saving Strategy for the Next Generation Cellular Wireless Systems

As mobile stations (MSs) in the next generation cellular wireless systems will more frequently operate on multiple applications, such as web browsing, VoIP, online video etc., energy saving becomes more critical and face new challenges from the quality of service (QoS) requirements. A special operation state, called sleep mode, is designed for energy saving of MS, in which MS operates on continuous sleep cycles, where every sleep cycle is the sum of a listening window and a sleep window. This paper proposes an energy saving method that adaptively determines sleep cycles and shifts listening window. When MS is in sleep mode, the sleep cycles are extended by probabilistic decisions related to the traffic statistic attributes. We also introduce the energy saving strategy for the frames by mixing best effort and persistent allocation traffic. The frequency of sleep cycles is used as one of the parameters for QoS purpose. Different from conventional methods, listening window can be shifted for shorter response time in our method. Extensive simulation results validate the advantages of our method both in terms of energy saving and shorter response time.