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Predictable 802.11 Packet Delivery fromWireless Channel Measurements
Daniel HalperinWenjun Hu, Anmol Sheth, David Wetherall
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
802.11 Wi-Fi technology
• Fast - 600 Mbps in 802.11n represents a 300x speedup in 12 years
• Reliable - vehicular speeds, extended range, stable hardware and software
• Ubiquitous - few dollars per chip allows integration everywhere
2
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
New, exciting apps on the horizon
802.11 Wi-Fi technology
• Fast - 600 Mbps in 802.11n represents a 300x speedup in 12 years
• Reliable - vehicular speeds, extended range, stable hardware and software
• Ubiquitous - few dollars per chip allows integration everywhere
2
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
New apps stress network
3
WirelessDisplay
WirelessInput
MobileWireless
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
New apps stress network
3
All-wirelessHome
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
New apps stress network
3
All-wirelessHome
Performance really matters
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Performance – in theory
39Mbps
4
Channel Measurements
Textbook Algorithms
Rate Selection
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Performance – in theory
39Mbps
4
Channel Measurements
Textbook Algorithms
Rate Selection
In practice, this has never worked!
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Performance – In practice
65 Mbps?Nope!
65 Mbps?Nope!
65 Mbps?Nope!
52 Mbps?Nope!
13 Mbps?Okay!
5
Statistics-based Adaptation
Problem:Convergence time
• Dynamic environments
• Large search spaces– >300 tx configs in 802.11n– Combined rate & power
Both are trends
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Goals: BridgingTheory and Practice
• Accurately predict performance over real channels
• Agile response to changing channels
• Leverage measurements available in real NICs
• Extend to 802.11n and more applications
6
Key: an accurate channel metric
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Goals: BridgingTheory and Practice
• Accurately predict performance over real channels
• Agile response to changing channels
• Leverage measurements available in real NICs
• Extend to 802.11n and more applications
6
Key: an accurate channel metric
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Today’s talk
• Why it’s hard to predict performance withRF measurements today
• Our solution: an accurate channel metric using Effective SNR
• Evaluation of Effective SNR in Wi-Fi Networks
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Today’s talk
• Why it’s hard to predict performance withRF measurements today
• Our solution: an accurate channel metric using Effective SNR
• Evaluation of Effective SNR in Wi-Fi Networks
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
SNR based on RSSI
• Received Signal Strength Indicator– Measures total power received in packet– With Noise, gives SNR for packet
• Treated as if directly reflects performanceE.g., NIC manufacturers list per-rate ‘sensitivity’
0 5 10 15 20 25 300
20
40
60
80
100
Packet−level SNR (dB)
PRR
6.51319.526395258.565
9
• In practice, SNR at which a rate starts to work can vary more than 10 dB for real links
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
802.11: OFDM and MIMOOrthogonal FrequencyDivision Multiplexing
Multiple-InputMultiple-Output
Frequency-selective fading Spatial diversity
10
Power
Frequency
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
802.11: OFDM and MIMOOrthogonal FrequencyDivision Multiplexing
Multiple-InputMultiple-Output
Frequency-selective fading Spatial diversity
10
Key: Different subchannelshave different SNRs
Power
Frequency
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
5
15
25
35
-28 -14 0 14 28
SNR
(dB)
Subcarrier index
Packet SNR for 4 faded links
11
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
5
15
25
35
-28 -14 0 14 28
SNR
(dB)
Subcarrier index
Packet SNR for 4 faded links
11
30 dB
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
5
15
25
35
-28 -14 0 14 28
SNR
(dB)
Subcarrier index
Packet SNR for 4 faded links
11
30 dB
17 dB
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Packet SNR
5
15
25
35
-28 -14 0 14 28
SNR
(dB)
Subcarrier index
Packet SNR for 4 faded links
11
30 dB
17 dB
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Packet SNR
5
15
25
35
-28 -14 0 14 28
SNR
(dB)
Subcarrier index
Packet SNR for 4 faded links
11
30 dB
17 dB
Errors
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Packet SNR
5
15
25
35
-28 -14 0 14 28
SNR
(dB)
Subcarrier index
Packet SNR for 4 faded links
11
30 dB
17 dB
Errors
Fundamental SNR mismatch
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
An 802.11n opportunity
• 802.11n provides detailed channel measurementsUsed for advanced MIMO techniques
• Channel State Information (CSI) measuresMIMO and OFDM!– Matrix captures per-antenna paths– One matrix per subcarrier
• Can we use it to predict packet delivery?In theory? In practice?
12
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Today’s talk
• Why it’s hard to predict performance withRF measurements today
• Our solution: an accurate channel metric using Effective SNR
• Evaluation of Effective SNR in Wi-Fi Networks
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Effective SNR• Introduced by Nanda and Rege in 1998
• Packet SNR: total power in the link
• Effective SNR: useful power in the link
14
5
15
25
35
-28 -14 0 14 28
SNR
(dB)
Subcarrier index
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Effective SNR• Introduced by Nanda and Rege in 1998
• Packet SNR: total power in the link
• Effective SNR: useful power in the link
14
5
15
25
35
-28 -14 0 14 28
SNR
(dB)
Subcarrier index
Effective SNR
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Using Effective SNR
39Mbps
15
Channel Measurements
Textbook Algorithms
Rate Selection
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Using Effective SNR
39Mbps
16
Textbook Algorithms
Rate Selection
Channel State Information(MIMO & OFDM)
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Using Effective SNR
17
39Mbps
Rate Selection
Channel State Information(MIMO & OFDM) Effective
SNR Model
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Using Effective SNR
18
Channel State Information(MIMO & OFDM) Effective
SNR Model
Working Configurations;Application Decision
1x65 ✘1x52 ✘2x26 ✔3x13 ✔
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Using Effective SNR
19
Channel State Information(MIMO & OFDM) Effective
SNR Model
Working Configurations;Application Decision
1x65 ✘1x52 ✘2x26 ✔3x13 ✔
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
• RX measures CSI from packet preambleNICs do this for MIMO/OFDM operation
• For every received frameMeasures all antennas + subcarriers used
Obtaining CSI
20
3-antenna Link
✹ ✹ ✹
✹ ✹ ✹
✹ ✹ ✹
3x3 Matrix
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
✹ ✹ ✹
✹ ✹ ✹
✹ ✹ ✹
One matrixper Subcarrier
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Using Effective SNR
21
Channel State Information(MIMO & OFDM) Effective
SNR Model
Working Configurations;Application Decision
1x65 ✘1x52 ✘2x26 ✔3x13 ✔
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Computing Effective SNR
• Single antenna link (1x1)CSI gives the per-symbol SNR
• Multiple RX antennas (1xN)Maximal-ratio combining
• MIMO link (MxN)Minimum mean-square error (MMSE)
A B
D E
A B
D E
A B
D E
A B
D E
A B
D E
A B
D E
A B
D E
A B
D E
✹ ✹
✹ ✹
22
Compute SNRs
per symbolCSI
SNRs
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Computing Effective SNRA B
D E
A B
D E
A B
D E
A B
D E
A B
D E
A B
D E
A B
D E
A B
D E
✹ ✹
✹ ✹
23
ComputeBERs
per symbol
SNRs
BERs
CSI
Modulation BER(ρ)
BPSK
QPSK
QAM-16
QAM-64
Q��
2ρ�
Q (√ρ)
Q��
ρ/5�
Q��
ρ/21�
Textbookformulas
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
BEReff
Computing Effective SNRA B
D E
A B
D E
A B
D E
A B
D E
A B
D E
A B
D E
A B
D E
A B
D E
✹ ✹
✹ ✹
24
Average:Effective BER
SNRs
BERs
CSI
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Computing Effective SNRA B
D E
A B
D E
A B
D E
A B
D E
A B
D E
A B
D E
A B
D E
A B
D E
✹ ✹
✹ ✹
25
SNRs
BERs
CSI
SNReffConvert back to SNR
BEReff
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Using Effective SNR
26
Channel State Information(MIMO & OFDM) Effective
SNR Model
Working Configurations;Application Decision
1x65 ✘1x52 ✘2x26 ✔3x13 ✔
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Predicting Packet Delivery
27
• Effective SNR thresholds for each rate
• Threshold per NIC implementation,not per NIC or per channel
• Adds flexibility to handle real NICs
• Hard vs soft decoding
• Other special techniquese.g., use optimal Maximum Likelihood receiveronly for small modulations
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Example Applications
• Rate/MIMO/Channel width selection:What is the fastest configuration for this link?
28
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
✹ ✹ ✹
✹ ✹ ✹
✹ ✹ ✹
3x3, 40 MHz
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Example Applications
• Rate/MIMO/Channel width selection:What is the fastest configuration for this link?
28
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
✹ ✹ ✹
✹ ✹ ✹
✹ ✹ ✹
3x3, 40 MHz
1x3
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Example Applications
• Rate/MIMO/Channel width selection:What is the fastest configuration for this link?
28
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
✹ ✹ ✹
✹ ✹ ✹
✹ ✹ ✹
3x3, 40 MHz
2x3
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Example Applications
• Rate/MIMO/Channel width selection:What is the fastest configuration for this link?
28
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
✹ ✹ ✹
✹ ✹ ✹
✹ ✹ ✹
3x3, 40 MHz
3x3
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Example Applications
• Rate/MIMO/Channel width selection:What is the fastest configuration for this link?
28
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
✹ ✹ ✹
✹ ✹ ✹
✹ ✹ ✹
3x3, 40 MHz
20 MHz40 MHz
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Example Applications
• Power Consumption:Which receive antenna is best to disable to save power?
29
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
✹ ✹ ✹
✹ ✹ ✹
✹ ✹ ✹
3x3, 40 MHz
RX AntennaSelection
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Example Applications
• Spatial Reuse:What is the lowest transmit power at which I can support 100 Mbps bitrate?
30
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
A B C
D E F
G H I
✹ ✹ ✹
✹ ✹ ✹
✹ ✹ ✹
3x3, 40 MHz
Power ×
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Today’s talk
• Why it’s hard to predict performance withRF measurements
• Our solution building a better metric using Effective SNR
• Evaluation of Effective SNR in Wi-Fi Networks
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Implemented in Intel NIC
• Intel Wi-Fi Link 5300 NIC (3x3, 450 Mbps)
• Two testbeds with > 200 widely varying links
• Linux (2.6.35-rc3) open source iwlwifi driver
• Firmware debug mode: send CSI to RX host
• Real-time computation: ~4 µs per 3x3 CSI32
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Evaluation Questions
• Does Effective SNR accurately predict packet delivery?
• Does an Effective SNR rate selection algorithm perform well?
• More results in the paper
• Wireless link transition region• Transmit power control• Collisions
33
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Predicting Optimal 3x3 Rate
34
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Predicting Optimal 3x3 Rate
34
0
13
26
52
65
0 10 20 30 40 50 60
Rat
e / s
tream
(Mbp
s)
SNR (dB)Packet SNR (dB)
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Predicting Optimal 3x3 Rate
34
0
13
26
52
65
0 10 20 30 40 50 60
Rat
e / s
tream
(Mbp
s)
SNR (dB)Packet SNR (dB)
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Predicting Optimal 3x3 Rate
35
0
13
26
52
65
0 10 20 30 40 50 60
Rat
e / s
tream
(Mbp
s)
SNR (dB)Effective SNR (dB)
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Rate control evaluation
• 802.11a: Does Effective SNR match related work?ESNR versus SampleRate, SoftRate, OPT
• 802.11n: Does Effective SNR extend to 802.11n?ESNR versus OPT
• Channel simulation over mobile traceto compare against related work & vary speed
• MATLAB simulation + SoftRate GNU Radio
• Effective SNR algorithm gets corrupted CSI
36
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu 37
Effective SNR for 802.11a
• Matches or beats 802.11a algorithms
• All within 15% of OPT
0
10
20
30
40
50
60
70
0 50 100 150 200 250
Avg
. deliv
ere
d r
ate
(M
bps)
Packet trace index (x100)
ESNRSampleRate
SoftRateSampleRate fixedSampleRate fixed retry
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu 37
Effective SNR for 802.11a
• Matches or beats 802.11a algorithms
• All within 15% of OPT
0
10
20
30
40
50
60
70
0 50 100 150 200 250
Avg
. deliv
ere
d r
ate
(M
bps)
Packet trace index (x100)
ESNRSampleRate
SoftRateSampleRate fixedSampleRate fixed retry
No rate fallback on retries:50% performance gap
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
ESNR extends to MIMO
• 80% accuracy, 10% overselection
• 24 rates vs 8, larger gap vs Previous-OPT38
0
50
100
150
200
0 50 100 150 200
Avg
. d
eliv
ere
d r
ate
(M
bp
s)
Packet trace index (x400)
OPTPrevious-OPT
ESNR
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Related work
39
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Related work802.11a MIMO &
Ant Sel.TX
PowerChannelWidth
RealNICs
SoftRate (2009)
AccuRate (2010)
Error Estim. Codes (2010)Effective
SNR
✔
✔ ✔ ✔
✔ ✔
✔ ✔ ✔ ✔ ✔
39
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Related work802.11a MIMO &
Ant Sel.TX
PowerChannelWidth
RealNICs
SoftRate (2009)
AccuRate (2010)
Error Estim. Codes (2010)Effective
SNR
✔
✔ ✔ ✔
✔ ✔
✔ ✔ ✔ ✔ ✔
39
Daniel Halperin, SIGCOMM 2010, dhalperi@cs.washington.edu
Conclusions
• For the first time, we can usemeasurements available in real NICs topredict packet delivery over real channels
• Matches good performance of existing rate adaptation algorithms and extends to 802.11n
• Applies to a broad problem space and provides a simple, practical API for protocols
• Lots more in the paper!
40
Thanks! Questions?dhalperi@cs.washington.edu
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