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

Model-Driven Energy-Aware
Rate Adaptation
M. Owais Khan, Vacha Dave, Yi-Chao Chen,
Oliver Jensen, Lili Qiu, Apurv Bhartia,
Swati Rallapalli
The University of Texas at Austin
MobiHoc 2013, Bangalore, India
1
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
RateThree
adaptation
needs
Antennas
2.10 W to energy-aware!
1.60 W
What’s the big deal?
• Fixed antenna systems are fairly simple
𝑒𝑛𝑒𝑟𝑔𝑦𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑 ∝ 𝐸𝑇𝑇
• Energy-aware rate adaptation becomes simple
Highest rate
Lowest ETT
Minimum
energy!
• Can this be applied to MIMO as well?
𝑒𝑛𝑒𝑟𝑔𝑦𝑚𝑢𝑙𝑡𝑖−𝑎𝑛𝑡𝑒𝑛𝑛𝑎𝑠 ≫ 𝑒𝑛𝑒𝑟𝑔𝑦𝑠𝑖𝑛𝑔𝑙𝑒−𝑎𝑛𝑡𝑒𝑛𝑛𝑎
– Additional hardware and RF chains
– But multiple data streams reduces transmission time!
Energy vs. Tx time: the trade-off
% decrease required
in tx time
Reduce time
by 68%! 70
3x3 Ant
2x2 Ant
65
60
55
Reduce time
by 50%!
50
45
40
35
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Single antenna tx time (ms)
• Exact rate and # of antennas depend on multiple factors
– Channel condition, wireless card and frame size
1. No single setting to minimize energy
2. Single antenna ≠ minimum energy
Hence, our work!
Understand how energy consumption in
these devices relates to these factors
Design an energy-aware rate adaptation
scheme that can minimize energy!
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
• Multiple wireless cards
– Intel 5300N series
– Atheros 11n
– Windows mobile smartphone
• Monsoon power monitor
– One reading/μs
– Maximum power value every 200μs
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
1.2
0.6
0.5
0.8
Energy (J)
Energy (J)
1
0.6
0.4
3 Ant
2 Ant
1 Ant
0.2
0
0
0.5
1
1.5
0.4
0.3
0.2
3 Ant
2 Ant
1 Ant
0.1
0
2
2.5
0
0.2
0.4
0.6
0.8
1
Transmission Time (S)
Transmission Time (S)
Atheros Wi-Fi transmitter
Atheros Wi-Fi receiver
𝑬𝒏𝒆𝒓𝒈𝒚𝒄𝒐𝒏𝒔𝒖𝒎𝒆𝒅 ∝ 𝑬𝑻𝑻
Slope of the line depends on # of antennas
3
3.5
2.5
3
2
2.5
Energy (J)
Energy (J)
Intel Energy Measurements
1.5
1
0
0
0.5
1
1.5
1.5
3 Ant,1 Streams
3 Ant,2 Streams
3 Ant,3 Streams
2 Ant,1 Streams
2 Ant,2 Streams
1 Ant,1 Streams
1
3 Ant,3 Streams
2 Ant,2 Streams
1 Ant,1 Streams
0.5
2
0.5
2
2.5
Transmission Time (S)
Intel Wi-Fi transmitter
0
0
0.5
1
1.5
2
Transmission Time (S)
Intel Wi-Fi receiver
𝑬𝒏𝒆𝒓𝒈𝒚𝒄𝒐𝒏𝒔𝒖𝒎𝒆𝒅 ∝ 𝑬𝑻𝑻
Slope of the line depends on # of antennas
2.5
Measurement-Driven Energy Model
• Use least-square fitting to develop energy models
𝐸𝑡𝑥 = 𝐴 × 𝐸𝑇𝑇 + 𝐵
𝐸𝑟𝑥 = 𝐶 × 𝐸𝑇𝑇 + 𝐷
where 𝐴, 𝐵, 𝐶, 𝐷 vary for different wireless cards
Intel
Atheros
𝐴 0.24 × 𝑛𝑡𝑥 + 0.425 × 𝑀𝐼𝑀𝑂 + 1.02
0.38 × 𝑛𝑡𝑥 + 0.108
𝐵
0.045 × 𝑛𝑡𝑥 + 0.108
0.040 × 𝑛𝑡𝑥 + 0.062
𝐶
0.30 × 𝑛𝑟𝑥 + 0.61
0.142 × 𝑛𝑟𝑥 + 0.30
𝐷
0.064 × 𝑛𝑟𝑥 + 0.167
0.048 × 𝑛𝑟𝑥 + 0.106
Validating the model
𝑥 − 𝑥′
𝑀𝐴𝑃𝐸 = 𝑚𝑒𝑎𝑛(
)
𝑥
𝑥 : actual energy consumption
𝑥 ′ : estimated energy consumption
Card
Atheros
Intel
Phone
Transmission
3.4%
0.65%
4.9%
Reception
1.3%
1.4%
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:
𝑀𝑀𝑆𝐸
𝑆𝑁𝑅𝑚
𝐸𝑠
=
𝑁𝑡 𝑁0
[𝐻𝐻 𝐻 +
1
𝐸𝑠
𝑁𝑡 𝑁0
−1
𝐼]−1
Compute loss rate
• Map pp-SNR to un-coded BER using known relationship
BERuncoded £ c1 ´Q
(
c2 ´ SNR
• Convert un-coded BER to coded BER
BERcoded ( r ) =
¥
å
a d ×Pd ( r )
d=d free
• Calculate frame error rate (FER)
FER =1- (1- BERcoded )L
)
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)
𝑠𝑖𝑧𝑒
1
𝐸𝑇𝑇 =
𝑟𝑎𝑡𝑒 (1 − 𝐹𝐸𝑅)
• Select the MCS with the minimum energy
– Different variations of schemes are possible
• Partial packet recovery (PPR) support
– Only the ETT calculation changes (ref. paper)
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
• 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 (Static)
14%-24%
17%-31%
1-10%
1-2%
10%-22%
51
66
46
Throughput (Mbps)
76
Energy (nJ/bit)
56
46
36
26
16
1-19%
0-1%
1-2%
41
36
31
26
21
16
11
6
6
Trace 1
Trace 2
Trace 3
Trace 1
Trace 2
Trace 3
Minimum Energy provides significant power savings
21
Intel Receiver (Static)
25-35%
26-42%
1-10%
1-4%
10%-26%
86
76
76
Throughput (Mbps)
86
Energy (nJ/bit)
66
56
46
36
26
46
36
26
6
6
Trace 3
1-5%
56
16
Trace 2
0-1%
66
16
Trace 1
1-23%
Trace 1
Trace 2
Trace 3
22
Intel Receiver (Mobile)
29-31%
34-40%
1-16%
2-6%
106
96
86
76
66
56
46
36
26
16
6
15-19%
2-13%
0-2%
3-6%
Energy (nJ/bit)
Throughput (Mbps)
41
36
31
26
21
16
11
6
Trace 1
Trace 2
Trace 3
Trace 1
Trace 2
Trace 3
23
Intel Receiver with PPR
76
Minimum Energy
Maximum Tput
w/ 80% Tput constraint
w/ 60% Tput constraint
26% to 28%
~9%
Trace 1
Trace 2
Throughput (Mbps)
Energy (nJ/bit)
66
56
46
36
26
16
6
Trace 3
56
51
46
41
36
31
26
21
16
11
6
26% to 29%
Trace 1
~9%
Trace 2
Trace 3
Minimum Energy provides significant power savings
24
Always use single-antenna systems?
1 antenna
2 antennas
3 antennas
1.0
0.8
0.6
0.4
0.2
0.0
1000
2000
3000
4000
Packet size (Bytes)
5000
Single Antenna performance decreases with increasing packet size
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
Minimum Energy
w/ 80% Tput constraint
76
19%
6%
28%
16%
51
Throughput (Mbps)
66
Energy (nJ/bit)
Maximum Tput
w/ 60% Tput constraint
56
46
36
26
16
24%
11%
22%
2%
46
41
36
31
26
21
16
11
6
Transmitter
Receiver
6
Transmitter
Receiver
27
Mobile Channel
Energy savings do not degrade with the channel!
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
29
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!
[email protected]