practical conflict graphs for dynamic spectrum distribution
DESCRIPTION
Practical Conflict Graphs for Dynamic Spectrum Distribution. Xia Zhou , Zengbin Zhang, Gang Wang, Xiaoxiao Yu * , Ben Y. Zhao and Haitao Zheng Department of Computer Science, UC Santa Barbara * Tsinghua University, China. Inefficient Spectrum Distribution. - PowerPoint PPT PresentationTRANSCRIPT
Practical Conflict Graphs for Dynamic Spectrum Distribution
Xia Zhou, Zengbin Zhang, Gang Wang, Xiaoxiao Yu*, Ben Y. Zhao and Haitao Zheng
Department of Computer Science, UC Santa Barbara
*Tsinghua University, China
2
Inefficient Spectrum Distribution
• Explosive wireless traffic growth
• The well-know problem: artificial spectrum shortage– Spectrum is assigned statically– Hard to get new spectrum– Current spectrum utilization is low
Need efficient spectrum distribution
3
Dynamic Spectrum Distribution
• Key requirements– Reuse spectrum in
space whenever possible
– Exclusive spectrum access for allocated users
Spectrum
?
??B
C
A
Must characterize interference conditions among users
4
Conflict Graphs
• Binary representation of pairwise interference conditions
CB
A
B C
A
Coverage area: all receiver locations
5
Benefits of Conflict Graphs
• Simple abstraction– Reduce spectrum allocation to graph
coloring problems
• Leverage numerous graph algorithms– Many efficient allocation algorithms
• Widely used
6
Key Issues on Conflict Graphs
• Hard to get it accurate– Wireless propagation is complex– Exhaustive measurements are not scalable– Solutions w/o measurements give errors, poor
performance
• Fail to capture accumulative interference– A fundamental graph limitation– Interference cumulate from multiple
transmissions
C
A
B
#1
#2
Are conflict graphs useful in practice?
7
Overview
• Goal: understand practical usability of conflict graphs
• Contributions– A practical method of building conflict graphs
– Measurement validation of graph accuracy
– Graph augmentation to address accumulative interference
8
Outline
• Introduction
• Measurement-Calibrated Conflict Graphs
• Validation Results
• Graph Augmentation
9
Building Practical Conflict Graphs
• Our approach: measurement-calibrated conflict graphs
Measurement overhead
Accuracy
Exhaustive measurement
sNon-
measurement methods
Our Goal
10
Measurement-Calibrated Conflict Graphs
Calibrated Propagation
Model
Predicted Signal Maps
Estimated Conflict Graph
Sampled Signal
Measurements
Exhaustive Signal
Measurements
Measured Conflict Graph
Monitor
?
11
Evaluating Conflict Graphs
• Compare estimated and measured conflict graphs
Exhaustive Signal
Measurements
Measured Conflict Graph
Spectrum Allocation
Results
Spectrum Allocation Benchmar
ks
Graph Similarit
y
Signal Predictio
n Accuracy
Sampled Signal
Measurements
Calibrated Propagatio
n Model
Predicted Signal Maps
Estimated Conflict Graph
Spectrum Allocation
Results
Monitor
12
Measurement Datasets
• Exhaustive signal measurements at outdoor WiFi networks
• Our own dataset collected at GoogleWifi– Capture weak signals using radio with higher
sensitivity
Dataset Location
Area
(km2)
#of APs
Avg # of APs heard
per location
# of measuredlocations
MetroFi Portland, OR 7 70 2.3 30,991
TFA Network
Houston, TX 3 22 2.7 27,855
GoogleWiFi
Mountain View,
CA7 78 6.2 11,447
13
Outline
• Introduction
• Measurement-Calibrated Conflict Graphs
• Validation Results
• Graph Augmentation
14
Evaluating Conflict Graphs
Exhaustive Signal
Measurements
Measured Conflict Graph
Spectrum Allocation
Results
Spectrum Allocation Benchmar
ks
Graph Similari
ty
Signal Predicti
on Accurac
y
Predicted Signal Maps
Estimated Conflict Graph
Spectrum Allocation
Results
15
Signal Prediction Results• Predict signal values using a sample of
measurements– Models: Uniform, Two-Ray, Terrain, and Street– Street model achieves the best accuracy
• Location-dependent pattern in prediction errors
0 0.1 0.2 0.3 0.4 0.50
1000
2000
3000
4000 Underprediction Overprediction
Distance to AP (km)
# o
f occ
urr
en
ces
Overpredict RSS values at farther locations
Underpredict RSS values at closer locations
16
Evaluating Conflict Graphs
Exhaustive Signal
Measurements
Measured Conflict Graph
Spectrum Allocation
Results
Spectrum Allocation Benchmar
ks
Graph Similari
ty
Signal Predicti
on Accurac
y
Predicted Signal Maps
Estimated Conflict Graph
Spectrum Allocation
Results
17
Conflict Graph Accuracy
• Extra edge: in estimated graph but not measured graph
• Missing edge: in measured graph but not estimated graph
Correct edgeExtra edgeMissing edge
Extra edges dominate!
18
Why Do Extra Edges Dominate?
• Signal prediction errors are location-dependent– An edge exists if Signal-to-Interference-and-Noise
Ratios (SINRs) < a threshold
SINR = Interfere
nce+ Noise
Signal
Under-estimate receivers’ SINR values more conflict edges
19
Evaluating Conflict Graphs
Exhaustive Signal
Measurements
Measured Conflict Graph
Spectrum Allocation
Results
Spectrum Allocation Benchmar
ks
Graph Similari
ty
Signal Predicti
on Accurac
y
Predicted Signal Maps
Estimated Conflict Graph
Spectrum Allocation
Results
Utilization
Reliability
20
Spectrum Allocation Benchmarks
• Estimated graphs are conservative
• Estimated graphs has lower spectrum utilization– Utilization: spectrum reuse
• Estimated graph has higher reliability– Reliability: % of users receive reliable spectrum use– Still, users suffer accumulative interference
Need to address accumulative interference!
21
Graph Augmentation
• Key idea: add edges selectively to improve reliability
• Our solution: greedy augmentation– Integrate spectrum allocation to identify edges to add– More details in the paper
• Result: 96%+ users receive reliable spectrum use
22
Our Conclusion:
Conflict Graphs Work!
23
BACKUP
24
Collecting GoogleWifi Dataset
• 3-day wardriving• 3 co-located laptops, each monitoring one
channel• Locations have 5m separation on average
25
Impact of Sampling Rate
• 34 monitors per km2 achieve the best tradeoff for the urban street environment
• Determine sampling rate– Depends on AP density, propagation
environment, and monitor’s sensitivity
26
Signal Prediction Errors
• Errors are noticeable, Gaussian distribution– Align with prior studies
27
Building Conflict Graphs
• Coverage-based conflict graph– Node: a spectrum user with its coverage region– Edge: eAB exists if when A and B use the same
channel, A or B fails to maintain γ of its receptions successful
Interference
ISignal
S
Reception succeeds if SINR is above a threshold
A B
28
Spectrum Allocation Benchmarks
• Allocation algorithm– Multi-channel allocation: maximize proportional
fairness
• Metric #1: spectrum efficiency– Average fraction of spectrum received per user
Extraneous edges lead to moderate efficiency loss (<
30%)
29
Spectrum Allocation Benchmarks
• Metric #2: spectrum reliability– Fraction of users with exclusive spectrum usage– Consider interference from all the others on the
same channel
Extraneous edges reduce the impact of accumulative
interference
Need to address accumulative interference!
30
Graph Augmentation Results
• Augmentation improves graph accuracy– Some edges added in measured graph are already
in estimated graph
31
Efficacy of Graph Augmentation
• Address accumulative interference– Eliminate reliability violations for measured graphs– 96+% reliability for estimated graphs– Add minimal edges, leading to efficiency loss <
15% for estimated graph