Presentation on theme: "Practical Conflict Graphs for Dynamic Spectrum Distribution Xia Zhou, Zengbin Zhang, Gang Wang, Xiaoxiao Yu *, Ben Y. Zhao and Haitao Zheng Department."— Presentation transcript:
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 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 2 Need efficient spectrum distribution
Dynamic Spectrum Distribution Key requirements – Reuse spectrum in space whenever possible – Exclusive spectrum access for allocated users 3 Spectru m ? ? ? B C A Must characterize interference conditions among users
Conflict Graphs Binary representation of pairwise interference conditions 4 C B A B C A Coverage area: all receiver locations
Benefits of Conflict Graphs Simple abstraction –Reduce spectrum allocation to graph coloring problems Leverage numerous graph algorithms –Many efficient allocation algorithms Widely used 5
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 6 C A B Are conflict graphs useful in practice?
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 7
Measurement-Calibrated Conflict Graphs 10 Calibrated Propagation Model Predicted Signal Maps Estimated Conflict Graph Sampled Signal Measurements Exhaustive Signal Measurements Measured Conflict Graph Monitor ?
Evaluating Conflict Graphs Compare estimated and measured conflict graphs 11 Exhaustive Signal Measurements Measured Conflict Graph Spectrum Allocation Results Spectrum Allocation Benchmark s Graph Similarity Signal Prediction Accuracy Sampled Signal Measurements Calibrated Propagation Model Predicted Signal Maps Estimated Conflict Graph Spectrum Allocation Results Monitor
Measurement Datasets Exhaustive signal measurements at outdoor WiFi networks Our own dataset collected at GoogleWifi –Capture weak signals using radio with higher sensitivity 12 DatasetLocation Area (km 2 ) #of APs Avg # of APs heard per location # of measured locations MetroFi Portland, OR ,991 TFA Network Houston, TX ,855 GoogleWiF i Mountain View, CA ,447
Evaluating Conflict Graphs 14 Exhaustive Signal Measurements Measured Conflict Graph Spectrum Allocation Results Spectrum Allocation Benchmark s Graph Similarity Signal Predictio n Accuracy Predicted Signal Maps Estimated Conflict Graph Spectrum Allocation Results
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 15 Overpredict RSS values at farther locations Underpredict RSS values at closer locations
Evaluating Conflict Graphs 16 Exhaustive Signal Measurements Measured Conflict Graph Spectrum Allocation Results Spectrum Allocation Benchmark s Graph Similarity Signal Predictio n Accuracy Predicted Signal Maps Estimated Conflict Graph Spectrum Allocation Results
Conflict Graph Accuracy Extra edge: in estimated graph but not measured graph Missing edge: in measured graph but not estimated graph 17 Correct edge Extra edge Missing edge Extra edges dominate!
Why Do Extra Edges Dominate? Signal prediction errors are location-dependent –An edge exists if Signal-to-Interference-and-Noise Ratios (SINRs) < a threshold 18 SINR = Interferenc e + Noise Signal Under-estimate receivers’ SINR values more conflict edges
Evaluating Conflict Graphs 19 Exhaustive Signal Measurements Measured Conflict Graph Spectrum Allocation Results Spectrum Allocation Benchmark s Graph Similarity Signal Predictio n Accuracy Predicted Signal Maps Estimated Conflict Graph Spectrum Allocation Results Utilization Reliability
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 20 Need to address accumulative interference!
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 21
Our Conclusion: 22 Conflict Graphs Work!
Collecting GoogleWifi Dataset 3-day wardriving 3 co-located laptops, each monitoring one channel Locations have 5m separation on average 24
Impact of Sampling Rate 34 monitors per km 2 achieve the best tradeoff for the urban street environment Determine sampling rate –Depends on AP density, propagation environment, and monitor’s sensitivity 25
Signal Prediction Errors Errors are noticeable, Gaussian distribution –Align with prior studies 26
Building Conflict Graphs Coverage-based conflict graph –Node: a spectrum user with its coverage region –Edge: e AB exists if when A and B use the same channel, A or B fails to maintain γ of its receptions successful 27 Interference I Signal S Reception succeeds if SINR is above a threshold A B
Spectrum Allocation Benchmarks Allocation algorithm –Multi-channel allocation: maximize proportional fairness Metric #1: spectrum efficiency –Average fraction of spectrum received per user 28 Extraneous edges lead to moderate efficiency loss (< 30%)
Spectrum Allocation Benchmarks Metric #2: spectrum reliability –Fraction of users with exclusive spectrum usage –Consider interference from all the others on the same channel 29 Extraneous edges reduce the impact of accumulative interference Need to address accumulative interference!
Graph Augmentation Results Augmentation improves graph accuracy –Some edges added in measured graph are already in estimated graph 30
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 31