1. DISCERN: Cooperative Whitespace Scanning in Practical Environments Tarun Bansal, Bo Chen and Prasun Sinha Ohio State Univeristy

2. Challenge : Limited Capacity due to Growing Demand 20X - 40X OVER THE NEXT FIVE YEARS 50 BILLION CONNECTED DEVICES BY 2020 35X 2009 LEVELS BY 2014 24 HOURS UPLOADED EVERY 60 SECONDS Slide courtesy of “White Space Networking: The Road Ahead” by Ranveer Chandra, Microsoft Research 2

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 3

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 4

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

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 6 PU 1 PU 2 n1n1 n2n2 n3n3 n4n4 n6n6 n5n5 SBS

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 n 1 and n 2 to scan PU 1 and n 3 to scan PU 2 – Optimal Solution: {n 1, n 2, n 3 } 7 PU 1 PU 2 n1n1 n2n2 n3n3 n4n4 n6n6 n5n5 SBS

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: {n 1, n 3, n 6 } Does not cover PU 1 with high accuracy 9 PU 1 PU 2 n1n1 n2n2 n3n3 n4n4 n6n6 n5n5 SBS

10. Existing Solutions: SUs with high RSSI of the PU signal Selected SUs: {n 3, n 4, n 5 } Does not cover PU 1 10 PU 1 PU 2 n1n1 n2n2 n3n3 n4n4 n6n6 n5n5 SBS

11. Existing Solutions: SUs with minimum correlation among themselves Selected SUs: {n 1, n 3, n 6 } Does not cover PU 1 with high accuracy Existing solutions are incapable of accounting for practical considerations. 11 PU 1 PU 2 n1n1 n2n2 n3n3 n4n4 n6n6 n5n5 SBS

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 12

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 n 5 reports: {1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1} n 6 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 13 n6n6 n5n5 SBS

14. Knowledge Factor – Knowledge Factor: Knowledge added by n i to n j about the state of the PU – Assume n i and n j are in the range of same PU with If x j = 0, then P(x i =0 ) is high – would be low K ij would be low – If n i and n j are in the range of same PU, then at least one of K ij or K ji would be low 14

15. Knowledge Factor When n i and n j are in the range of different PU K ij ≈ K ji ≈ 1 Value of knowledge factor allows DISCERN to differentiate the relationship between the two SUs 15

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 16

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 (P i d and P i f ) – SUs in S may cooperate – Do not know how many PUs are there 17

18. Using cooperation Probability that n i can predict the state of the PU in the range of n j (∆ j ) – Depends upon Probability that n i and n j are in the range of same PU – Given by P ij (Probability that n i and n j are in the range of same PU) How accurate is n i itself – Given by P i d −P i f – Accuracy of n i in predicting the state of ∆ j is given by: P ij (P i d −P i f ) 18 ∆ j nini njnj

19. Accuracy of predicting the state of a PU But n j can take help from all other SUs as well Ω(S,k,j) = Probability that SUs in S can cooperatively predict the state of the PU in the range of n j 19 Primary User njnj n1n1 n2n2 n3n3

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 c k after aggregating readings from SUs in S 21

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

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 23

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

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 25

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%. 26

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 27

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 28

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.). 29

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. 30

31. Questions

32. Sensing overview Discern is useful in both types of scannings Use scanning to determine free channels Transmit on free channels Frequently scans the channel in use to ensure they are free

33. 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 33 |S| < ρ