Efficient Clustering of Cognitive Radio Networks Using Affinity Propagation

1. Efficient Clustering of Cognitive Radio Networks Using Affinity Propagation Lee, JiSeon February 1 , 2011 Pohang University of Science and Technology (POSTECH)

2. Reference • K.E. Baddour, O. Ureten, T.J. Willink., “Efficient Clustering of Cognitive Radio Networks Using Affinity Propagation”, Computer Communications and Networks, 2009. ICCCN 2009. Proceedings of 18th International Conference on • Communications Research Centre, Ottawa, ON, Canada • Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points.” , Science Vol. 315, pp. 972–976, February 2007 • University of Toronto

3. Introduction • Goal • Efficiently group nodes in an ad hoc cognitive radio networks • Objectives of clustering • Cluster-based control structures provides more efficient use of resources for large dynamic networks • Interference resilience • Low communication overhead Scattered nodes in the network Ref. www.cs.ucf.edu/~turgut/COURSES/.../Lecture2-Jan14-clustering.ppt

4. Introduction • Goal • Efficiently group nodes in an ad hoc cognitive radio networks • Objectives of clustering • Cluster-based control structures provides more efficient use of resources for large dynamic networks • Interference resilience • Low communication overhead Clusterheads are identified Ref. www.cs.ucf.edu/~turgut/COURSES/.../Lecture2-Jan14-clustering.ppt

5. Introduction • Goal • Efficiently group nodes in an ad hoc cognitive radio networks • Objectives of clustering • Cluster-based control structures provides more efficient use of resources for large dynamic networks • Interference resilience • Low communication overhead Clusterheads are formed Ref. www.cs.ucf.edu/~turgut/COURSES/.../Lecture2-Jan14-clustering.ppt

6. Introduction • Goal • Efficiently group nodes in an ad hoc cognitive radio networks • Objectives of clustering • Cluster-based control structures provides more efficient use of resources for large dynamic networks • Interference resilience • Low communication overhead Clusters are connected Ref. www.cs.ucf.edu/~turgut/COURSES/.../Lecture2-Jan14-clustering.ppt

7. Proposed solution (1/2) • Decentralized cluster formation process • Affinity propagation (AP) message-passing technique • Cluster are formed by their similarity • Measure of similarity • Based on the number of common channels shared by secondary nodes • Improvement of cluster stability to primary user activations • and are the sets of channels that are available to secondary node and

8. Proposed solution (2/2) • Affinity propagation (AP) • Clustering algorithm originally proposed for finding a small subset of “exemplars” in large data sets • AP is initiated by simultaneously considering all nodes as potential header and responsibility and availability messages are passed between pair of nodes iteratively until a refined subset of eligible exemplars emerges • Maximized value of is selected Ref. http://video.google.com/videoplay?docid=-5932472040652321870#

9. Proposed solution (3/3) • Affinity propagation (AP) Ref. http://video.google.com/videoplay?docid=-5932472040652321870#

10. Simulation • Simulation parameter • Randomly generated open spectrum access scenarios • 3 primary user, 3 channel Interference caused to the PUs Number of clusters Robustness

11. Conclusions • A novel approach for forming clusters in CRNs based on the AP message-passing algorithm • finding a small number of clusters that covers the ad hoc networks • Depending on the application’s goal, just changes similarity value and then cluster formation is changed easily • Selecting irrelevant nodes in a group when using spatial diversity • Limitation • Too many message passing is needed in dynamic environment