1. Enhanced power efficient sleep mode operation for IEEE 802.16e based WiMAX Shengqing Zhu, and Tianlei Wang IEEE Mobile WiMAX Symposium, 2007 IEEE Mobile WiMAX Symposium, 2007

2. Outline Introduction System description An analytical model Dynamic tuning of initial sleep window Performance evaluation Conclusion

3. Introduction IEEE 802.16e (mobile WiMAX) is targeting for Mobile Subscriber Stations (MSSs) To efficiently manage energy in IEEE 802.16e systems –Sleep-mode operation

4. Motivation The analytical results show that there is a tradeoff between the power consumption and the mean delay –The key of the tradeoff is the initial sleep window Present a heuristic algorithm to tune the initial sleep window dynamically according to the traffic load

5. Three type of sleep mode operation in IEEE 802.16e Type 1 power saving class is recommended for connections of non-real-time variable rate traffic Type 2 is recommended for connections of real-time variable rate traffic Type 3 is recommended for multicast connections as well as for management operations

6. The overview of the IEEE 802.16e power management (Type 1 ) 2 n until reach its T max Fixed size

7. When some data to transmit in IEEE 802.16e sleep mode BS MSs L TkTk a SDU want to transmit MOB-TRF-IND L T k-1 awake mode Data BS MSs TkTk a SDU want to transmit L MOB-TRF-IND T k-1 awake mode Data Request Response delay QoS is mainly measured by mean delay

8. System description Type 1 power saving class Downlink BS transmits all packets addressed to the MS in its buffer as well as packets arriving to BS during the time the MS receives queued packets queue packets arriving t

9. System description Sleep interval Packet arrival process –Poisson process arrival rateλ –Ignored listening interval –Service time of a packet Generally distributed with PDF

10. Markov chain model for the sleep mechanism of IEEE 802.16e The behaviors of an MS working in power saving mode –bi-dimensional random process {s(t), b(t)} –β i represents the transition probability

11. An analytical model i,0i+1,0 βiβi M,0 M,sM,s 1- β M M-1,0 β M-1 βMβM i,0 i,si,s 1- β i 0,s1,sM,sM,s … 0,0 111

12. By means of An analytical model

13. Power consumption MS can be divided into two parts –Receiving the expected data packets = ρP r ρ = λE[v] (data rate * serving time) –Sleep operation

14. Delay When a packet arrives at the BS till the moment when the packet is successfully received by the related MS j packets addressed to the MS buffered in the BS after the ith sleep state The probability that there are j packets buffered after sleep state {i, 0} TiTi … j packets t

15. Delay The mean number of packets served in the i service state when j packets buffered after {i, 0} Average delay over the packets served in ith service state with j initial packets Average delay over all packets t j packets

16. Power Consumption vs. Delay

17. The power consumption versus initial sleep window

18. The mean delay versus initial sleep window

19. Dynamic tuning of initial sleep window If the initial sleep window is small a high traffic load leads to high power consumption –A traffic-aware initial sleep window tuning should be considered After serving all packets buffered, the MS revert to an initial sleep window which is dynamically tuned according to the number of packets served

20. Example T i+2 T i+1 TiTi 2323 10 packets > 2^3 T i+2 T i+1 TiTi 2 packets < 2^3 TiTi T i+2 t t

21. Performance evaluation Simulation tool: NS2 Simulation time: 50 hours Average value: 10 runs Compare with traditional standard: Tb = 40ms and M = 5

22. Power consumption comparison for various traffic load

23. Mean delay comparison for various traffic load

24. Conclusion Present a simple Markov chain model to investigate the performance of IEEE 802.16e sleep mode operation The analytical results show that the power consumption decreases with the initial sleep window but the mean delay increases with it Propose a heuristic algorithm, which tunes the initial sleep window dynamically according to the traffic load Simulation results show that the proposed algorithm can improve the power consumption significantly

25. Thanks!!