model-driven energy-aware rate adaptation m. owais khan, vacha dave, yi-chao chen oliver jensen,...

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Model-Driven Energy-Aware Rate Adaptation

M. Owais Khan, Vacha Dave, Yi-Chao ChenOliver Jensen, Lili Qiu, Apurv Bhartia

Swati Rallapalli

MobiHoc 2013, Bangalore, India

The University of Texas at Austin

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!

Mode Intel TX Intel Rx

Single Antenna 1.28 W 0.94 W

Two Antennas 1.99 W 1.27 W

Three Antennas 2.10 W 1.60 WRate adaptation needs to energy-aware!

What’s the big deal?• Fixed antenna systems are fairly simple

• Energy-aware rate adaptation becomes simple

Highest rate Lowest ETT Minimum energy!

• Can this be applied to MIMO as well?

– Additional hardware and RF chains– But multiple data streams reduces transmission time!

Energy vs. Tx time: the trade-offReduce time by 68%!

Reduce time by 50%!

1. No single setting to minimize energy2. Single antenna ≠ minimum energy

• Exact rate and # of antennas depend on multiple factors– Channel condition, wireless card and frame size

Hence, our work!

Understand energy consumption in these devices

Design an energy-aware rate adaptation scheme

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

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

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

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

Atheros Energy Measurements

Atheros Wi-Fi transmitter Atheros Wi-Fi receiver

Slope of the line depends on # of antennas

Intel Energy Measurements

Intel Wi-Fi transmitter Intel Wi-Fi receiver

Slope of the line depends on # of antennas

Measurement-Driven Energy Model

Intel Atheros

• Use least-square fitting to develop energy models

where vary for different wireless cards

Validating the model

: actual energy consumption: estimated energy consumption

Card Transmission ReceptionAtheros 3.4% 1.3%

Intel 0.65% 1.4%Phone 4.9% 3.6%

Error is consistently below 5%!

Energy Aware Rate Adaptation

Select rate for next transmission that minimized energy!

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:

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)

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

Putting it all together

Measure CSI

Calculate pp-SNR

Calculate estimated loss rate

Compute ETT

Select rate minimizes energy!

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

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

Intel Transmitter

Energy Throughput

MinEng consumes 14-24% less energy than MaxTput

Intel Receiver

Energy Throughput

MinEng consumes 25-35% less energy than MaxTput

Intel Receiver with PPR

Energy Throughput

MinEng consumes 26-28% less energy than MaxTput

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

Static Channel

Energy ThroughputMinEng consumes 19% less energy for transmitter and

28% for receiver

Mobile Channel

Energy savings do not degrade with the channel!

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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

• 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

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

Questions ???

Thank You.

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

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

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

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

Power Measurement Setup

iwl5300

Power Monitor

gnd

56 mΏ

Phone Energy Measurements

Smartphone transmitter Smartphone receiver

Slope of the line depends on # of antennas