Modeling, Analysis, and Design of Adaptive Power Saving Algorithms for 802.16 Sleep Mode Operations

• Main Authors: Ming-Fang Chen, 陳明芳
• Other Authors: 謝宏昀
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/50649039896140172476

碩士 === 國立臺灣大學 === 電信工程學研究所 === 96 === Power saving is an important issue for mobile stations (MSs). IEEE 802.16e defines three types of power saving classes (PSCs) for supporting the sleep mode operations on MSs with different types of traffic. Related work has developed analytical models to evaluate the performance of power saving operations. Most of them employ Poisson process or CBR as the traffic model, and hence their capability is limited in capturing the characteristics of realistic traffic. In this thesis, we first propose a generic analytic model for capturing the behaviors of IEEE 802.16 sleep mode operations under arbitrary traffic distribution, including Pareto and 4IPP traffic models for describing the characteristic of HTTP/FTP data traffic. While we focus primarily on the operation of PSC of type I, we also show that the proposed model can be extended to PSC of type II. Simulation results show that the proposed model has better flexibility and can achieve higher accuracy compared with existing models. While power saving operation can prolong the lifetime of MSs, one significant tradeoff is that they may potentially increase the packet transmission delay. Based on the proposed analytical model, we observe that the delay depends on setting of minimal/maximal sleep window size. If we can dynamically change the size of window depending on the traffic conditions, we can avoid delay from increasing with varying traffic distributions. To address the tradeoff between energy efficiency and packet delay, we propose an adaptive power saving algorithm for maintaining packet delay on MSs under different traffic loads. Through on-line observing the distribution of arriving packets, the algorithm can determine the optimal power saving parameter setting and adaptively change them according to varying traffic conditions. Simulations show that the proposed algorithm can indeed achieve minimal energy consumption and satisfy the desired delay constraint.