Transcript Slides

Accurate WiFi Packet Delivery Rate
Estimation and Applications
Owais Khan and Lili Qiu.
The University of Texas at Austin
Infocom 2016, San Francisco.
1
Frequency Diversity
30
20
10
0
-10 1
SNR (dB)
• Wireless channel are
frequency selective
~22dBs
10
20
30
• Bandwidth is increasing
with new standards
BW (MHz)
Channel Subcarriers
1825
2000
1500
1000
500
20
40
160
a/b/g
n
ac
0
ad
Wider Channels experience more frequency diversity
2
Challenges
• Frequency diversity results in bursty errors
– Lower SNR subcarriers have more errors
• Delivery Rate hard to predict for bursty errors even
with Channel State Information (CSI)
• Existing techniques do not perform well in bursty
errors
3
Limitation of Average SNR
Delivery Rate
• Avg. SNR is widely used for delivery rate estimation
12 dB Gap
Ref. Halperin10
SNR is not a good predictor in Freq. Selective Channels 4
Effective SNR
• Effective SNR shows better results than Avg. SNR
• Approach
1. Map the SNR per subcarrier to BER
2. Map BEReff,k back to Effective SNR
3. Use Effective SNR to select the appropriate rate
How Accurate is Effective SNR?
5
Effective SNR Accuracy
• Scatter plot of Estimated delivery rate vs. gnd. truth
Gnd Truth Delivery Ratio
95% 1
Gap of 56%
0.8
0.6
0.4
0.2
39%
0
0
0.2
0.4
0.6
0.8
EffSNR Delivery Ratio
1
Effective SNR is also not very accurate
6
Error Burstiness across Frame
• 802.11 interleaver does not uniformly distribute bits
1
CDF
0.8
65%
0.6
code rate: 1/2
0.4
0.2
0
0
• WiFi interleaver has a
skewed distribution
• Error still bursty in WiFi
interleaver
100
WiFi Interleaver
Ideal Interleaver
200
300
gap
Delivery rate estimation must incorporate error
burstiness
7
Contributions
Develop two new techniques to estimate delivery rate in bursty
errors
Propose a new Interleaver to reduce burstiness
8
Problem Formulation
• Goal
– To accurately predict delivery ratio using CSI information
• Options
1. Analytical modeling
•
Intractable for freq. selective channels.
2. Simulate online
•
Prohibitively expensive
Our Approach
3. Lookup table based approach
•
•
Pre compute delivery rate for error patterns
Error patterns capture the burstiness
4. Machine learning based approach
•
Use supervised learning to estimate delivery rate
9
Lookup Table: Idea
Analog Signal
Receive Chain
1, 0, 1, 1, 0, …
0.5+1.2j,
-2-0.8j,…
De-Modulation
De-Interleave
FEC Decode
Data
• Decoding success depend on error pattern to FEC Decoder
– Same errors have different outcome depending on location
Idea:
“Calculate the error probability of all possible patterns offline
and use them to estimate delivery rate for given CSI”
10
Lookup Table: Approach
• Run Viterbi decoding offline and build a lookup table
• Use error pattern as input
Prob. Error
0.1
0.9
…
…
Pattern
110000010011000000
110000011111000000
• Independent of wireless channel or hardware
• Using an error pattern for frame prohibitively expensive
11
Lookup Table: Observation
• Errors in frame decouple after a certain period
Error seq. 1
Error seq. 2
Independent!
• The de-coupling length is dependent on code rate
Code Rate
1/2
2/3
3/4
5/6
Window Size
75 bits
50 bits
50 bits
40 bits
2^40 is still a
very large
number!
12
Can we reduce size further?
• We find upper and lower error thresholds
– Errors less than lowerThresh, decoding always successful
– Errors greater than upperThresh, decoding always fails
Code
Rate
1/2
2/3
3/4
5/6
LowerThresh
UpperThresh
5
3
2
2
10
5
4
4
Complexity
reduces to
nChoosek
13
Actual Lookup Table
• To Summarize
1. Each code rate has a fixed window size
2. The table is bounded by number of errors in a window
• Implement the table as a hash map
key
3203
2907
Perr
0
0.75
…
…
Pattern
110000010011000000
110000011111000000
…
– The error sequence is converted to integer and used as key
How to use these tables to estimate delivery rate?
14
Delivery Rate Estimation
Calculate BER per subcarrier from SNR
Error Pattern
Generation
Generate Error Patterns from BER
Lookup Error Prob. for error pattern
Lookup next pattern in sequence
Take product of all Error Probs.
Repeat for multiple error sequences
15
Sliding window operation
• Example:
Window Size = 4
b 0 b 1 b 2 b 3 b 4 b 5 b 6 b 7 b 8 b 9 b 10 b 11 b 12 b 13 b 14 b 15
0 1 0 1 0 0 1 0 0 0 1
p1
p2
0
1
0
0
0
p3
Pe,1 = p1 x p2 x p3
• Delivery Rate is the average of Prob. of Error of N seqs
16
Machine Learning
• Motivation
– Avoids the time and space complexity of lookup tables
– Machine learning can provide faster online solution
• Machine learning algorithm
– We chose Neural Networks
• Reason
– Supports non-linear continuous functions
– Appropriate for delivery ratio
17
Feature Set
• Feature Set
– Use Bit error Rate per bit
• Advantage
– Easy to obtain from CSI information
– Allows de-coupling from the interleaver
– Feature size is limited by number of bits in OFDM symbol
18
Neural Network: Setup
• Multi Layer Perceptron
Neuron
Output Layer
Input Layer
Bit Error Rate
Hidden Layers
Delivery Rate
2 Layers
20 Neurons per layer
19
Neural Network Operation
Training
Input
Bit-Error-Rate
Delivery Ratio
Output
Neural Network
Network
weights
Intel Channel Traces
TGn Channel models
Input
Bit-Error-Rate
Testing
Trained Network
Output
Delivery
Rate
20
Contributions
Develop two new techniques to estimate delivery rate
Propose a new Interleaver
21
Standard Interleaver
• Spreads adjacent bits by four subcarriers
1
2
3
4
5
1
14
27
40
1
14
27 40
6
2
15
28
41
2
15
7
8
…
…
…
…
…
28 41
49 50
51 52
13
26
39
52
…
13 26
39 52
Subcarriers 4 spaces apart can experience similar fate
22
Proposed Interleaver
Key Ideas
1. Maximize the separation between likely corrupted bits
1. Use SNR information to identify more error prone bits
•
Interleaving pattern dependent on SNR
23
Proposed Interleaver
s0
s1
s2
s3
s4
s5
s6
s7
s8
s9
10 Subcarriers
Sorted based
on SNR
Maximize separation between bad locations
0
1
2
3
4
s0
5
6
7
8
9
s1
maximum separation
next ofdm symbol
24
Proposed Interleaver
s0
s1
s2
s3
s4
s5
s6
s7
s8
s9
10 Subcarriers
Sorted based
on SNR
Maximize separation between bad locations
0
1
s0
s4
2
3
4
5
6
7
8
9
s2 s8
s5
s1
s6
s3
s9
s7
b0 b1 b2 b3 b4 b5 b6 b7 b8 b9
maximum separation
Interleaver maximizes the separation between bad bits
25
Evaluation and Results
26
Evaluation Methodology
• Trace driven simulation to evaluate performance
• Traces collected using Intel 5300 WLAN card
• Trace Features
– Static and mobile traces
– Both 20Mhz and 40Mhz bandwidths
• Traces generated using IEEE-TGn Channel models
– Traces simulate a range of indoor and outdoor environments
– Include both 20Mhz and 40Mhz bandwidths
27
Evaluated Schemes
Schemes
Description
EffSNR
Average BER is used to estimate effective SNR
Lookup
Our lookup table based approach
ML
Trained neural network is used to estimate
delivery ratio
28
Delivery Rate Accuracy (40MHz)
1
CDF
0.8
0.6
0.4
0.2
0
0
0.2
EffSnr w/ WiFi Int.
EffSnr w/ Our Int.
Lookup w/ WiFi Int.
Lookup w/ Our Int.
ML. w/ WiFi Int.
ML. w/ Our Int.
0.4
0.6
0.8
EffSNR
Wifi: 11%
Our: 27%
Lookup
Wifi: 4.5%
Our: 3%
ML
Wifi: 6%
Our: 3%
Delivery Rate Error
Lookup and ML show improvement over Effective SNR
29
Interleaver Performance
40 MHz
1
1
0.8
0.8
0.6
0.6
CDF
CDF
20 MHz
0.4
WiFi Int.
Our. Int.
0.2
0
0
30
20
10
Throughput (Mbps)
17% impv.
40
0.4
0.2
0
0
WiFi Int.
Our. Int.
20
40
Throughput (Mbps)
60
22% impv.
Proposed interleaver outperforms 802.11 interleaver
30
Rate Adaptation
40 MHz
30
20
EffSNR w/ WiFi Int.
EffSNR w/ Our Int.
Lookup w/ WiFi Int.
Lookup w/ Our Int.
ML. w/ WiFi Int.
ML. w/ Our Int.
10
0
0
20
40
SNR
60
Lookup
Wifi: up to 62-75%
Our: up to 72-79%
80
Throughput(Mbps)
Throughput(Mbps)
20 MHz
40
30
EffSNR w/ WiFi Int.
EffSNR w/ Our Int.
Lookup w/ WiFi Int.
Lookup w/ Our Int.
ML. w/ WiFi Int.
ML. w/ Our Int.
20
10
0
0
20
40
SNR
80
60
ML
Wifi: up to 65-75%
Our: up to 72-79%
Significant throughput benefit at transition regions
31
Energy Savings Transmitter
20 MHz
EffSNR w/ WiFi Int.
EffSNR w/ Our Int.
Lookup w/ WiFi Int.
Lookup w/ Our Int.
ML. w/ WiFi Int.
ML. w/ Our Int.
400
300
200
100
0
0
20
40
SNR
60
Lookup
Wifi: up to 26-35%
Our: up to 29-36%
80
200
Energy(nJ/bit)
Energy(nJ/bit)
500
40 MHz
EffSNR w/ WiFi Int.
EffSNR w/ Our Int.
Lookup w/ WiFi Int.
Lookup w/ Our Int.
ML. w/ WiFi Int.
ML. w/ Our Int.
150
100
50
0
20
40
SNR
60
80
ML
Wifi: up to 27-35%
Our: up to 29-36%
Significant Energy savings at transition regions
32
Summary
• Identify the reasons for poor performance
• Propose Lookup and ML based approach to estimate
delivery ratio
– Reduce delivery rate error by 60% for 802.11 interleaver
and 88% for proposed interleaver
Future Work
– Explore other applications of machine learning in wireless
network management
33
THANK YOU!
34