1. Primary Social Behavior aware Routing and Scheduling for Cognitive Radio Networks Shouling Ji and Raheem Beyah Georgia Institute of Technology Zhipeng Cai Georgia State University Jing Selena He Kennesaw State University

2. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Introduction Primary Users (PUs) Secondary Users (SUs)

3. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Introduction Problem –Routing –Scheduling Motivation –Primary users’ activities follow some social patterns –The primary network consisting of cell phone users is more active during [10am – 10pm] compared with other time slots –The primary activity (cell phone users) is heavier during [9am – 5pm] in the business area while heavier during [6pm – 11pm] in the residential area Contribution –Primary social behavior-aware whitespace analysis –ε-optimal joint routing and scheduling algorithm –Distributed joint routing and scheduling framework

4. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Outline Introduction and Motivation System Model Social Behavior Analysis of PUs Joint Routing and Time-domain Scheduling Distributed Solution Simulation Conclusion

5. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs System Model Primary Network –N Poisson distributed PUs with density of λ –Transmission and interference radii: R and R I –The network time is slotted with each time slot of length τ Secondary Network –n i.i.d. SUs –Transmission and interference radii: r and r I Problem –Study how to route and schedule the data transmission for a set of communication sessions within time T

6. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Outline Introduction and Motivation System Model Social Behavior Analysis of PUs Joint Routing and Time-domain Scheduling Distributed Solution Simulation Conclusion

7. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Social Behavior Analysis of PUs The utilization of primary spectrum is inefficient! Q1: how does the spectrum whitespace/opportunity distribute over time? Q2: how to utilize primary spectrum effectively and meanwhile minimizing harmful impacts on primary activities?

8. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Social Behavior Analysis of PUs MIT Reality Trace –97 mobile device holders, 114046 Bluetooth contacts spanning 246 days UCSD Trace –275 mobile device holders, 123335 WiFi contacts spanning 77 days

9. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Social Behavior Analysis of PUs The probability that a PU is active during the t-th time slot The available whitespace for a secondary link

10. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Outline Introduction and Motivation System Model Social Behavior Analysis of PUs Joint Routing and Time-domain Scheduling Distributed Solution Simulation Conclusion

11. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Joint Routing and Scheduling Maximize a scaling factor κ such that each session l can achieve a data transmission rate of at least κγ(l) Scheduling Routing Flow Balance Constraint Capacity Constraint Mixed-Integer Non-Linear Program (MINLP)

12. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Joint Routing and Scheduling MINLP P Linearized version of P Divide P into two subproblems Solution

13. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Outline Introduction and Motivation System Model Social Behavior Analysis of PUs Joint Routing and Time-domain Scheduling Distributed Solution Simulation Conclusion

14. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Distributed Solution Forwarding Sector Forwarding score –Prefers the next-hop such that it is closer to the destination, more available bandwidth, less interference, and less traffic

15. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Distributed Solution

16. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Outline Introduction and Motivation System Model Social Behavior Analysis of PUs Joint Routing and Time-domain Scheduling Distributed Solution Simulation Conclusion

17. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Simulation Settings –Time slot: 1min –One periodical size: 24 hours = 1440 time slots –By default: W = 100, N = 100, n = 500, session# = 6, …… Comparison –Coolest (ICDCS’11): a spectrum mobility-aware routing algorithm for CRNs

18. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Simulation Available bandwidth for SUs Successful delivery ratio

19. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Conclusion –Primary social behavior-aware whitespace analysis for CRNs –Both centralized and distributed joint routing and scheduling algorithms for CRNs

20. S. Ji, Z. Cai, J. S. He, and R. BeyahRouting and Scheduling for CRNs Thank you! Shouling Ji sji@gatech.edu http://users.ece.gatech.edu/sji/