Model-Driven Energy-Aware Rate Adaptation

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

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! Rate adaptation needs to energy-aware!

3. What’s the big deal? • Fixed antenna systems are fairly simple • Energy-aware rate adaptation becomes simple • Can this be applied to MIMO as well? • Additional hardware and RF chains • But multiple data streams reduces transmission time!

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

5. Hence, our work!

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 • Multiple wireless cards • Intel 5300N series • Atheros 11n • Windows mobile smartphone • Monsoon power monitor • One reading/μs • Maximum power value every 200μs

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 transmitter Atheros Wi-Fi receiver Slope of the line depends on # of antennas

11. Intel Energy Measurements Intel Wi-Fi transmitter Intel Wi-Fi receiver Slope of the line depends on # of antennas

12. Measurement-Driven Energy Model • Use least-square fitting to develop energy models where vary for different wireless cards

13. Validating the model : actual energy consumption : estimated energy consumption Error is consistently below 5%!

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

15. Channel State Information (CSI) • Specifies amplitude and phase between tx-rx pair • Measured for all subcarriers using preamble • Reported once per received frame • pp-SNR can be calculated as:

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

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 Energy Throughput MinEng consumes 14-24% less energy than MaxTput

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

23. Intel Receiver with PPR Throughput Energy 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 Energy Throughput 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 • 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 Energy measurement and Models • 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 Rate Adaptation • Power Saving Mode Optimization [Napman10, Sleepwell11, E-mili11] • Complementary to our work 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 Power Monitor 56 mΏ gnd iwl5300

35. Phone Energy Measurements Smartphone receiver Smartphone transmitter Slope of the line depends on # of antennas