1. Model-Driven Energy-Aware Rate Adaptation M. Owais Khan, Vacha Dave, Yi-Chao Chen Oliver Jensen, Lili Qiu, Apurv Bhartia Swati Rallapalli 1 MobiHoc 2013, Bangalore, India The University of Texas at Austin

2. Motivation Multi-antenna devices are becoming common Offer diverse rate choices – # of antennas, modulation, coding, # of streams Rate adaptation – beaten to death problem? Large capacity gain, but significantly more energy! ModeIntel TXIntel Rx Single Antenna1.28 W0.94 W Two Antennas1.99 W1.27 W Three Antennas2.10 W1.60 W Rate adaptation needs to energy-aware!

3. What’s the big deal? Highest rateLowest ETT Minimum energy!

4. Energy vs. Tx time: the trade-off Reduce time by 68%! Reduce time by 50%! 1.No single setting to minimize energy 2.Single antenna ≠ minimum energy Exact rate and # of antennas depend on multiple factors – Channel condition, wireless card and frame size

5. Hence, our work! Understand energy consumption in these devices Design an energy-aware rate adaptation scheme

6. Contributions Extensive power measurements for multiple 802.11n wireless adapters Derive energy model based on power measurements Propose an energy-aware rate adaptation scheme Evaluate using simulation and testbed experiments

7. Why Model-Driven? Why not probing? – Slow given the large search space w/ MIMO – Hard to accurately measure the power of probe frames Model-driven – Estimate power consumption for each rate under the current channel condition – Directly select the one w/ lowest power

8. Power Measurement Setup Monsoon power monitor – One reading/μs – Maximum power value every 200μs Multiple wireless cards – Intel 5300N series – Atheros 11n – Windows mobile smartphone

9. Power Measurement Methodology Measurements at both transmitter and receiver Different configurations – Frame size (250-1500 bytes) – # of antennas – 802.11n compliant data rates

10. Atheros Energy Measurements Atheros Wi-Fi transmitterAtheros Wi-Fi receiver

11. Intel Energy Measurements Intel Wi-Fi transmitterIntel Wi-Fi receiver

12. Measurement-Driven Energy Model IntelAtheros

13. Validating the model CardTransmissionReception Atheros3.4%1.3% Intel0.65%1.4% Phone4.9%3.6% Error is consistently below 5%!

14. Energy Aware Rate Adaptation Select rate for next transmission that minimized energy!

15. Channel State Information (CSI)

16. Compute loss rate Map pp-SNR to un-coded BER using known relationship Convert un-coded BER to coded BER Calculate frame error rate (FER) Partial packet recovery (PPR) support – Only the ETT calculation changes (ref. paper)

17. Estimate energy consumption AP or back-end server keeps table of energy models – Account for most commonly used Wi-Fi cards Get the make/model of the Wi-Fi card – Explicit feedback or passive detection Compute ETT based on frame loss rate (FER) Get all MCS that can give 90% or more delivery rate – Select the one with minimum energy

18. Putting it all together Measure CSICalculate pp-SNRCalculate estimated loss rateCompute ETTSelect rate minimizes energy!

19. Evaluation Trace-driven simulator – Static and mobile channel traces using Intel 5300 – Written in python (??? LOC) Testbed – Uses the Intel 5300 card – Iwlwifi driver is modified to support rate adaptation

20. Simulation Methodology Developed in Python using real CSI traces Different schemes are supported – Sample Rate with MIMO – Effective SNR – Maximum throughput – Minimum energy – Minimum Energy with throughput constraint

21. Intel Transmitter EnergyThroughput MinEng consumes 14-24% less energy than MaxTput

22. Intel Receiver EnergyThroughput MinEng consumes 25-35% less energy than MaxTput

23. Intel Receiver with PPR Energy Throughput MinEng consumes 26-28% less energy than MaxTput

24. Testbed Implemented scheme on Intel Wi-Fi link 5300 driver – Used tool in [Halperin10] to extract CSI from driver Static channel – 200 UDP Packet of 1000 bytes each transmitted – Results averaged over 10 runs Mobile channel – Receiver moves away from transmitter at walking speed – Results averaged over 5 runs

25. Static Channel EnergyThroughput MinEng consumes 19% less energy for transmitter and 28% for receiver

26. Mobile Channel Energy savings do not degrade with the channel!

27. Related Work 27 Models based on data size [Carvahlo04], empirical study [Bala09] Neither considers effects of multiple antennas, data rates, tx power Study power consumption under different parameters[Halperin10] Do not develop energy model Extensively studied [Bicket05, Holland01, Sadeghi02, Wong06, etc.] None of these schemes consider minimizing energy Energy based rate adaptation [Li12] Limited effectiveness of probing-based approach Power Saving Mode Optimization [Napman10, Sleepwell11, E-mili11] Complementary to our work Energy measurement and Models Rate Adaptation Power Savings

28. Conclusion Collect and analyze extensive power measurements – Derive simple energy models for transmission/reception Develop model-driven energy-aware rate adaptation scheme Experimentally show significant energy savings possible – 14-37% over existing approaches – PPR extensions can be even better

29. Questions ??? Thank You.

30. Selecting the min Energy Rate SNR values are used to calculate the delivery ratio and expected transmission time ETT = PacketSize x DeliveryRatio transmission time Energy is calculated using the energy model – Appropriate transmission parameters like number of antennas – Expected transmission time – The rate which has the smallest estimated energy consumption is selected

31. Variations Minimize energy with throughput constraint – Selects a constraint on throughput. E.g. 80% of the maximum throughput possible for a given channel – Selects the rate which consumes the least amount of energy while satisfying the constraint on throughput Partial Packet Recovery Support – Approach also works with PPR – PPR only changes ETT calculation. The model remains the same

32. Simulator Following schemes are implemented – Sample Rate with MIMO Probing scheme. Uses loss rate as a metric to maximize throughput – Maximum Throughput Selects the rate which yields the highest throughput irrespective of energy consumption – Minimum Energy Selects the rate which consumes the least amount of energy – Minimum Energy with Throughput Constraint Tries to minimize energy consumption by placing a threshold on throughput loss

33. Multi-antenna Wi-Fi ETT vs. energy relationship does not hold! – Highest throughput ≠ lowest energy – Additional energy consumption by MIMO Single antenna does not always consume minimum energy Rate minimizing energy depends on channel condition and energy profile of Wi-Fi device Solution: Joint Optimization of Energy and Throughput through Rate Adaptation

34. Power Measurement Setup iwl5300 Power Monitor gnd 56 mΏ

35. Phone Energy Measurements Smartphone transmitter Smartphone receiver