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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

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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!

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

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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

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

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

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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

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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

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

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

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

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Measurement-Driven Energy Model IntelAtheros

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

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

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Channel State Information (CSI)

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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)

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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

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

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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

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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

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

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

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

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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

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

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Mobile Channel Energy savings do not degrade with the channel!

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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

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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

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Questions ??? Thank You.

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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

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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

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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

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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

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Power Measurement Setup iwl5300 Power Monitor gnd 56 mΏ

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Phone Energy Measurements Smartphone transmitter Smartphone receiver

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