2. Introduction
Secondary Users (SUs)
Primary Users (PUs)

3. Introduction Problem Contribution
Systematically analyze the spectrum whitespace in CRNs from a social network perspective
Contribution
Propose a novel metric named centrality score to measure the active weights of PUs by considering each PU’s topological importance and the global primary network running and traffic information
Analyzed the whitespace for SUs under three primary activity social patterns: i.i.d. distribution, Poisson distribution, and Gaussian distribution
Propose a Spectrum Altitudes based Virtual Backbone (SAVB) construction algorithm

4. Outline Introduction System Model
PUs’ Social Attributes and Whitespace Analysis
Virtual Backbone (VB) construction
Simulation
Conclusion

5. System Model Primary Network Secondary Network
N Poisson distributed PUs with density of λp
Transmission and interference radii: R and RI
The network time is slotted with each time slot of length τ
The licensed spectrum bandwidth W
The primary network is dense scaling
Secondary Network
n randomly deployed SUs
Transmission and interference radii: r and rI
The secondary network is connected and dense scaling

6. Outline Introduction System Model
PUs’ Social Attributes and Whitespace Analysis
Virtual Backbone (VB) construction
Simulation
Conclusion

7. Centrality Score of PUs
Motivation: local and global activity behavior of PUs
Centrality score of a PU Si
The betweenness centrality of Si: the number of shortest paths passing Si
Expected # of active PUs
The # of shortest paths in the primary network
The average # of PUs on shortest paths

8. Centrality Score of PUs
Centrality score of a PU Si
The average # of active PUs on each shortest path
The weight of a PU on each shortest path to be active
The normalized weight of Si to be active

9. Social Activity Pattern of PUs
By recent empirical studies
i.i.d. activity pattern
Poisson distribution pattern , e.g., the GSM90 uplink service
Gaussian distribution pattern , e.g., cell phone users, cellular network users, mobile device holders, WiFi users

10. Whitespace Analysis i.i.d. activity
The lower and upper bounds of the centrality score of PU Si
The lower and upper bounds of the active probability of PU Si
The lower and upper bounds of the available spectrum whitespace for a secondary link

11. Whitespace Analysis Poisson distributed pattern
Gaussian distributed pattern

12. Outline Introduction System Model
PUs’ Social Attributes and Whitespace Analysis
Virtual Backbone (VB) construction
Simulation
Conclusion

13. Virtual Backbone (VB) Construction
Definition of VB
A VB B of the secondary network is a subset of SUs such that (i) every SU is either in B or has some neighbor in B; and (ii) B is connected.
Objective
Seek a VB with small size
Seek a VB with more spectrum bandwidth available
Spectrum altitude
Measures the average available whitespace at SU su
A high implies su has more neighbors and high available spectrum whitespace to its neighbors on average

14. Virtual Backbone (VB) Construction
Seek a Maximal Independent Set (MIS) M of the secondary network
Final a minimal set C of connectors to connect the independent nodes in M

15. Virtual Backbone (VB) Construction
Correctness analysis of SAVB
Performance analysis of SAVB

16. Outline Introduction System Model
PUs’ Social Attributes and Whitespace Analysis
Virtual Backbone (VB) construction
Simulation
Conclusion

17. Simulation Settings Comparison
Time is slotted with each time slot is normalized to one
The primary bandwidth is normalized to 100
Examine the unicast throughput under different primary activity distributions
Comparison
US (Journal of Combinatorial Optimization, 2013): a VB is first constructed on top of a cell-based network partition; then, the unicast is carried out on the VB

18. Simulation
Whitespace estimation
Successful delivery ratio

19. Conclusion
We provide a mathematical framework for deriving the whitespace for SUs
With local and global primary activity consideration
With typical primary social patterns consideration
Propose a spectrum altitude based virtual backbone construction algorithm

20. Thank you!
Shouling Ji