practical conflict graphs for dynamic spectrum distribution

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