Practical Conflict Graphs for Dynamic Spectrum Distribution

1. Practical Conflict Graphs for Dynamic Spectrum Distribution Xia Zhou, Zengbin Zhang, Gang Wang, XiaoxiaoYu*, Ben Y. Zhao and HaitaoZheng 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 ? A C ? ? B Must characterize interference conditions among users

4. Conflict Graphs • Binary representation of pairwise interference conditions Coverage area: all receiver locations A A C B C B

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 #1 • 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 #2 C A Are conflict graphs useful in practice? B

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 Our Goal Exhaustive measurements Accuracy Non-measurement methods Measurement overhead

10. Measurement-Calibrated Conflict Graphs Exhaustive Signal Measurements Measured Conflict Graph Monitor Estimated Conflict Graph Sampled Signal Measurements Predicted Signal Maps Calibrated Propagation Model ?

11. Evaluating Conflict Graphs • Compare estimated and measured conflict graphs Monitor Exhaustive Signal Measurements Spectrum Allocation Results Measured Conflict Graph Spectrum Allocation Benchmarks Signal Prediction Accuracy Graph Similarity Estimated Conflict Graph Spectrum Allocation Results Sampled Signal Measurements Predicted Signal Maps Calibrated Propagation Model

12. Measurement Datasets • Exhaustive signal measurements at outdoor WiFi networks • Our own dataset collected at GoogleWifi • Capture weak signals using radio with higher sensitivity

13. Outline • Introduction • Measurement-Calibrated Conflict Graphs • Validation Results • Graph Augmentation

14. Evaluating Conflict Graphs Exhaustive Signal Measurements Spectrum Allocation Results Measured Conflict Graph Spectrum Allocation Benchmarks Signal Prediction Accuracy Estimated Conflict Graph Graph Similarity Spectrum Allocation Results Predicted Signal Maps

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 UnderpredictRSS values at closer locations Overpredict RSS values at farther locations

16. Evaluating Conflict Graphs Exhaustive Signal Measurements Spectrum Allocation Results Measured Conflict Graph Spectrum Allocation Benchmarks Signal Prediction Accuracy Estimated Conflict Graph Graph Similarity Spectrum Allocation Results Predicted Signal Maps

17. Conflict Graph Accuracy • Extra edge: in estimated graph but not measured graph • Missing edge: in measured graph but not estimated graph • Extra edges dominate! Correct edge Extra edge Missing edge

18. Why Do Extra Edges Dominate? • Signal prediction errors are location-dependent • An edge exists ifSignal-to-Interference-and-Noise Ratios (SINRs) < a threshold Signal SINR = Interference + Noise Under-estimate receivers’ SINR values  more conflict edges

19. Evaluating Conflict Graphs Exhaustive Signal Measurements Spectrum Allocation Results Measured Conflict Graph Utilization Reliability Spectrum Allocation Benchmarks Signal Prediction Accuracy Estimated Conflict Graph Graph Similarity Spectrum Allocation Results Predicted Signal Maps

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:eABexists if when A and B use the same channel, A or B fails to maintain γof its receptions successful A B InterferenceI Signal S Reception succeeds if SINR is above a threshold

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 • Eliminatereliability violations for measured graphs • 96+% reliability for estimated graphs • Add minimal edges, leading to efficiency loss < 15% for estimated graph