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
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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!
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!
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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!
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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
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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 802.11n
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 (250-1500 bytes)– # of antennas– 802.11n compliant data rates
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Atheros Energy Measurements
Atheros Wi-Fi transmitter Atheros Wi-Fi receiver
Slope of the line depends on # of antennas
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Intel Energy Measurements
Intel Wi-Fi transmitter Intel Wi-Fi receiver
Slope of the line depends on # of antennas
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Measurement-Driven Energy Model
Intel Atheros
• Use least-square fitting to develop energy models
where vary for different wireless cards
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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%!
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Energy Aware Rate Adaptation
Select rate for next transmission that minimized energy!
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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:
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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 CSI
Calculate pp-SNR
Calculate estimated loss rate
Compute ETT
Select 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
Energy Throughput
MinEng consumes 14-24% less energy than MaxTput
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Intel Receiver
Energy Throughput
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
Energy ThroughputMinEng 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• 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
Slope of the line depends on # of antennas