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DISCERN: Cooperative Whitespace Scanning in Practical Environments

DISCERN: Cooperative Whitespace Scanning in Practical Environments. Tarun Bansal , Bo Chen and Prasun Sinha Ohio State Univeristy. Challenge : Limited Capacity due to Growing Demand. 24 HOURS UPLOADED EVERY 60 SECONDS. 20X - 40X OVER THE NEXT FIVE YEARS. 50 BILLION CONNECTED DEVICES

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DISCERN: Cooperative Whitespace Scanning in Practical Environments

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  1. DISCERN: Cooperative Whitespace Scanning in Practical Environments TarunBansal, Bo Chen and PrasunSinha Ohio State Univeristy

  2. Challenge : Limited Capacity due to Growing Demand 24 HOURS UPLOADED EVERY 60 SECONDS 20X - 40X OVER THE NEXT FIVE YEARS 50 BILLION CONNECTED DEVICES BY 2020 35X 2009 LEVELS BY 2014 • VideoUploads • Streaming VideoIncreasing Wireless Demand • Devices Proliferation • Mobile Data Traffic Slide courtesyof “White Space Networking: The Road Ahead” by Ranveer Chandra, Microsoft Research

  3. White Space Channels • Discrepancy in channel usage • Unlicensed (ISM) bands are congested • Licensed bands are free most of the time • What if unused channels are used for data transmission? Taken from “How much white-space capacity is there?” IEEE DySPAN, 2010

  4. Opportunistic Usage • Unlicensed users must avoid interference to licensed user (or primary user, PU) • Scan frequently to detect arrival of primary user • Scanning takes time and results in throughput loss • Scanning must be reliable • Use Cooperation

  5. Problem Statement • Multiple SUs available to scan multiple channels Develop a solution that computes scanning assignment S S = { (ni, cj): ni scans channel cj} Subject to • Strict budget constraints in terms of time allocated for scanning: |S| < ρ • Take into account practical considerations

  6. Practical Considerations • Presence of obstacles • Multiple PUs per channel • Must select SUs such that all PUs are covered • Can aggregate readings of only those SUs that are in the range of same PU SBS n6 n1 n5 n2 n4 n3 PU1 PU2

  7. Which user should scan • Budget constraint: SBS has to select 3 SUs • Optimal solution: • Must cover both PUs and take into account presence of obstacle • Use n1 and n2 to scan PU1 and n3 to scan PU2 • Optimal Solution: {n1, n2, n3} SBS n6 n1 n5 n2 n4 n3 PU1 PU2

  8. Do existing solutions work? • Three existing solutions • Maximize coverage (Geographical Select) • SUs with high RSSI of the PU signal (Min et al.) • SUs with minimum correlation among themselves (Cacciapuoti et al.)

  9. Existing Solutions: Maximize coverage Selected SUs: {n1, n3, n6} Does not cover PU1 with high accuracy SBS n6 n1 n5 n2 n4 n3 PU1 PU2

  10. Existing Solutions: SUs with high RSSI of the PU signal Selected SUs: {n3, n4, n5} Does not cover PU1 SBS n6 n1 n5 n2 n4 n3 PU1 PU2

  11. Existing Solutions: SUs with minimum correlation among themselves Selected SUs: {n1, n3, n6} Does not cover PU1 with high accuracy Existing solutions are incapable of accounting for practical considerations. SBS n6 n1 n5 n2 n4 n3 PU1 PU2

  12. DISCERN Overview • Step 1: Differentiate SUs that are in the range of same PU • Handles presence of multiple PUs • Step 2: Define a metric that quantifies the scanning accuracy of an assignment • Step 3: Greedy algorithm to compute the scanning assignment

  13. DISCERN Step 1 • Differentiate SUs that are in the range of same PU • Given two SUs, are they in the range of same PU? • Difficult since SUs in the range of same PU may have low correlation • Say n5 reports: {1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1} • n6 reports: {1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0} • Correlation = 0.169 (Not high enough) • Need a new metric to determine if two SUs are in the range of same PU • Between 0 and 1: 0 when two SUs are definitely in range of different PU, 1 when two SUs are definitely in the range of same PU n6 SBS n5

  14. Knowledge Factor • Knowledge Factor: Knowledge added by ni to nj about the state of the PU • Assume ni and nj are in the range of same PU with • If xj = 0, then P(xi =0 ) is high • would be low • Kij would be low • If ni and nj are in the range of same PU, then at least one of Kij or Kji would be low

  15. Knowledge Factor • When ni and nj are in the range of different PU • Kij ≈ Kji≈ 1 • Value of knowledge factor allows DISCERN to differentiate the relationship between the two SUs

  16. DISCERN overview • Step 1: Differentiate SUs that are in the range of same PU • Step 2: Define metric that quantifies the scanning accuracy of an assignment • Handles differences in the accuracy of different SUs • Step 3: Greedy algorithm to compute the scanning assignment

  17. Ω (S) -metric • Metric that computes the effectiveness of a scanning assignment • Denoted by Ω(S) • Higher Ω(S) implies that channel state estimation based on S is correct • Challenge • SUs in S have different accuracies (Pid and Pif) • SUs in S may cooperate • Do not know how many PUs are there

  18. Using cooperation • Probability that nican predict the state of the PU in the range of nj(∆j) • Depends upon • Probability that niand nj are in the range of same PU • Given by Pij (Probability that ni and nj are in the range of same PU) • How accurate is ni itself • Given by Pid−Pif • Accuracy of niin predicting the state of ∆j is given by: Pij(Pid−Pif) nj ∆ j ni

  19. Accuracy of predicting the state of a PU • But nj can take help from all other SUs as well • Ω(S,k,j) = Probability that SUs in Scan cooperatively predict the state of the PU in the range of nj nj n1 n3 Primary User n2

  20. Accuracy of predicting the state of a PU • Ω(S,k,j) should be between 0 and 1 • Ω(S,k,j) should be 1 if accuracy of any SU in S is 1 • With increase in the cardinality of S, Ω(S,k,j) should increase since more observations about the state of ∆j are available.

  21. Accuracy of predicting the state of all PUs over a single channel • Ω (S,k) = Probability of correctly estimating the state of the channel ck after aggregating readings from SUs in S

  22. Computing the metric over all channels • Ω (S) = Average over all channels

  23. DISCERN overview • Step 1: Differentiate SUs that are in the range of same PU • Step 2: Define metric that quantifies the scanning accuracy of an assignment • Step 3: Greedy algorithm to compute the scanning assignment

  24. Greedy Algorithm to compute S • Add pairs of (ni, ck) to S • At every step, add (ni, ck) that maximizes the value of Ω(S) • Using submodular optimization technique, we bound the approximation ratio by 0.63

  25. Experiments: Setup • To show correctness of knowledge factor • Two USRP nodes placed at different locations • Collect data over multiple channels • Four different scenarios that capture different relationship of the two nodes

  26. Setup and Results • Scenario 1: Both SUs adjacent to each other on the roof of a 8-floor building • Scenario 2: One SU is in the basement while the other is on the roof • Scenario 3: Both SUs are in the basement of the 8-floor building • Scenario 4: One SU is on the roof of the building , other is in an open parking lot at a distance of 80 miles. • We observed that correlation with optimal threshold correctly classified the SUs in 69% cases while knowledge factor in 95% cases. • Knowledge factor improves the accuracy by over 25%.

  27. Simulations setup • Trace-driven simulations • SBS located at the center and varying number SUs were randomly deployed around it in a circular field of 20 miles. • Channel Model: 10 channels • PU Model: 40 PUs

  28. Other Algorithms • Geographical Select: Algorithm selects that SU for scanning which has the maximum distance from the already selected nodes • Min et al.: Selects nodes with the highest received signal strength (RSS) of the PU signal • Cacciapuoti et al.: Selects nodes that have minimum correlation with each other

  29. Simulation Results • Variation with SU density On average, DISCERN improves the accuracy by at least 30% (Geographical Select), 130% (Min et al.) and 40% (Cacciapuoti et al.).

  30. Conclusion • Novel knowledge based mechanism • Using this knowledge based method, defined a metric (Ω) that captures the accuracy of a given scanning assignment • Experiments show that Discern improves the accuracy of determining if two SUs are in the range of the same PU by over 25% • Simulations show that Discern improves the accuracy of channel state estimation by at least 30% when compared to other algorithms.

  31. Questions

  32. Simulations setup • Trace-driven simulations • SBS located at the center and 300 SUs were randomly deployed around it in a circular field of 20 miles. • Channel Model • 10 channels • Slow fading and fast fading • PU Model • 40 PUs on these 10 channels within a radial distance of 20 miles from the center • PU location and their power level established using FCC database • PU on/off state using traces collected using USRP radio |S| < ρ

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