Optimal Distributed Data Collection for Asynchronous Cognitive Radio Networks

1. Optimal Distributed Data Collection for Asynchronous Cognitive Radio Networks ZhipengCai, ShoulingJi, Jing (Selena) He, Anu G. Bourgeois Georgia State University

2. OUTLINE 1 Introduction System Model 2 Distributed Data Collection 3 4 Simulation and Analysis 5 Conclusion

3. Introduction

4. Introduction • Cognitive Radio Networks (CRNs) • The utilization of spectrum assigned to licensed users varies from 15% to 85% temporally and geographically (FCC report) • Unlicensed users (Secondary Users, SUs) can sense and learn the communication environment, and opportunistically access the spectrum without causing any unacceptable interference to licensed users (Primary Users, PUs)

5. Introduction • Why Distributed Algorithms? • CRNs tend to be large-scale distributed systems • CRNs are dynamic Systems • Spectrum opportunities are dynamic with respect to time and space • Challenges • How to guarantee secondary network activities do not hurt primary network activities? • How to make decision based on only local information? • How to overcome problems induced by lack of time synchronization? • How to theoretically analyze the performance of distributed algorithms?

6. Introduction • Contributions • Derive a Proper Carrier-sensing Range (PCR) under the physical interference modelfor Secondary Users (SUs) • Propose an order-optimal Asynchronous Distributed Data Collection (ADDC) algorithm • Simulations are conducted to validate ADDC

7. System Model

8. System Model • Primary Network • Nindependent and identically distributed (i.i.d.) PUs • Locally finite property • Working power • Network time is slotted with slot length • During each time slot, each PU transmits data with probability

9. System Model • Secondary Network • n SUs and one base station • Maximum transmission radius of SUs is r • The secondary network can be represented by graph • Conditions on communication between two SUs

10. System Model • Data Collection • At a particular time slot t, every SU produces a data packet of size B • The set of all the n data packets produced by SUs at time t is called a snapshot • The task of gathering all the n data packets of a snapshot to the base station without any data aggregation is called a data collection task • The data collection delay is the time consumption to finish a data collection task • The data collection capacity is the average data receiving rate at the base station during a data collection process

11. System Model • Interference Model • Physical interference model • For PUs • For SUs

12. Distributed Data Collection

13. Distributed Data Collection • Data Collection Tree • Proper Carrier-sensing Range (PCR) • Data Collection Algorithm • Performance Analysis

14. Data Collection Tree • Connected Dominating Set (CDS) based Data Collection Tree

15. Proper Carrier-sensing Range • Objectives • The secondary network does not cause unacceptable interference to the activities of the primary network • All the SUs transmitting data simultaneously are interference-free • The carrier-sensing range is as small as possible, which implies SUs can obtain more spectrum opportunities

16. Proper Carrier-sensing Range si • Concurrent Set: a set of active nodes s.t. all the nodes in this set can conduct data transmission simultaneously. • : • Proper Carrier-sensing Range (PCR): the carrier-sensing range R is a PCR if for any R-set, it is aconcurrent set.

17. Proper Carrier-sensing Range • How to decide the proper carrier-sensing range (PCR)? • In a R-Set, to guarantee SUs will not cause unacceptable interference to PUs, it is sufficient to have (Lemma 2) • In a R-Set, to guarantee SUs can transmit data simultaneously and interference-freely, it is sufficient to have (Lemma 3) • We can set the PCR , where

18. Proper Carrier-sensing Range

19. Data Collection Algorithm • Asynchronous Distributed Data Collection (ADDC) algorithm

20. Performance Analysis • The number of dominators and connectors within the PCR of an SU is upper bounded by , where is a function on x with (Lemma 5) • The number of SUs within the PCR of an SU is upper bounded by , and with probability 1.(Lemma 6) • The expected time for an SU to obtain a spectrum opportunity is where . (Lemma 7) • Any SU having data for transmission can transmit at least one data packet to its parent within time . (Theorem 1)

21. Performance Analysis • The delay induced delay by the proposed Asynchronous Distributed Data Collection (ADDA) algorithm is upper bounded by This implies the achievable data collection capacity of ADDC is which is order-optimal. (Theorem 2)

22. Simulation

23. Simulation • Network setting • An i.i.d. primary network • An i.i.d secondary network • Please refer to the paper for detailed settings • Compared algorithm • Coolest (ICDCS 2011): the path with the most balanced and/or the lowest spectrum utilization by PUs is preferred for data transmission

24. Simulation • Data Collection Delay vs. Network Size (n and N)

25. Simulation • Data Collection Delay vs. and

26. Simulation • Data Collection Delay vs. Transmission Power

27. Conclusion • We study the distributed data collection problem in CRNs • We propose an Asynchronous Distributed Data Collection (ADDC) algorithm for CRNs, which is order-optimal • Simulations are conducted to validate the performance of ADDC

28. Optimal Distributed Data Collection for Asynchronous Cognitive Radio Networks ZhipengCai, ShoulingJi, Jing (Selena) He, Anu G. Bourgeois Georgia State University Thank you!