Spectrum Opportunity-Based Control Channel Assignment in Cognitive Radio Networks

1. Spectrum Opportunity-Based Control Channel Assignment in Cognitive Radio Networks Loukas Lazos, Sisi Liu and Marwan Krunz ECE Dept., University of Arizona, Tucson Presented by Loukas Lazos SECON 2009, Rome, Italy

2. The Promises of Cognitive Radio Technology orthogonal frequency bands CRA PRA CRB CRC PRB • Solve two critical problems • Spectrum scarcity – Exploit dynamic spectrum opportunities • Interoperability – Communicate with their own kind and other radio technologies • Co-existence with legacy users (Primary radios) • CRs must obey regulatory rules – Higher priority to Primary Radio (PR) users • Policy enforcement heavily coupled with CR hardware and protocol design Loukas Lazos, SECON 2009, University of Arizona

3. Cooperative Diversity CRD CRC CRA PR1 PR2 CRE CRB orthogonal frequency bands Exchange individual sensing observations to define idle channels Share idle channels – need a mechanism for negotiation The existence of a coordination (control) channel is required! Nodes cooperate in the spectrum sensing process Loukas Lazos, SECON 2009, University of Arizona

4. Current Practices for Control Channel Assignment Already overcrowded Uncontrolled interference No guaranteed performance • Use unlicensed bands such as ISM bands Loukas Lazos, SECON 2009, University of Arizona

5. Current Practices for Control Channel Assignment Finding an unoccupied frequency band is a challenge We need a fixed licensed frequency band to build dynamic spectrum allocation systems FCC Cognitive Radio Advocates • Allocate a slice of spectrum for carrying control traffic • Contradicts the open spectrum architecture Loukas Lazos, SECON 2009, University of Arizona

6. Dynamic Control Channel Assignment • Allocate one of the idle channels for control • Creates the following circular dependency Loukas Lazos, SECON 2009, University of Arizona

7. Further Challenges orthogonal frequency bands • Spectrum opportunities vary with location and time • Leads to a partition of the network into clusters • Need for dynamic migration of the control channel based on PR activity • Need for inter-cluster coordination Loukas Lazos, SECON 2009, University of Arizona

8. Spectrum-opportunity Based Assignment • Five-step process • Sense idle channels • Discover neighbors (in the absence of a common channel) • Exchange idle channel list • Agree on a common time schedule for the control-channel location • Migrate control channel if a PR user occupies the current one CRD CRC CRA PR CRB CRE Loukas Lazos, SECON 2009, University of Arizona

9. Neighbor Discovery • In the absence of a control channel, CRs may reside in different frequency bands • Construct a universal time-slotted schedule • Each CRii individually determines the list of idle channels Cii = { i1,…,ik } • A CRi i beacons its channel list Cion channel ij Ci during slots t= 1, 2,… if ij = [(t-1) (mod M)]+1, and stays silent otherwise (M: number of channels). • Any CRk ` that hears CRi’s transmission places CRi in Nk • CRk i communicates with CRi Nk using the channel schedule derived from Ciiuntil a common control channel is setup. Loukas Lazos, SECON 2009, University of Arizona

10. Neighbor Discovery • Construct a universal time-slotted schedule t 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 t CRA 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 t CRB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 t CRC 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 wasted slots : 50% efficiency orthogonal frequency bands (M) Loukas Lazos, SECON 2009, University of Arizona

11. Time Synchronization Issue t 1 2 3 4 5 6 7 8 9 t CRA 1 2 3 4 5 6 7 8 9 t CRB 1 2 3 4 5 6 7 8 9 t CRC 1 2 3 4 5 6 7 8 9 orthogonal frequency bands (M) Time synchronization need not be tight Loukas Lazos, SECON 2009, University of Arizona

12. Cluster-based Control Channel Assignment • Partition the network into clusters • Take into account local idle channels Loukas Lazos, SECON 2009, University of Arizona

13. Mapping Clustering to a Graph Problem(1) Info available at CR A Network connectivity graph Each CRi constructs a bipartite graph Gi(Ai, Bi, Ei) Combine network topology with idle channel availability Loukas Lazos, SECON 2009, University of Arizona

14. Mapping Clustering to a Graph Problem(2) A bicliqueQi(Xi, Yi) represents a cluster with membership Xi where channels Yi are common to all cluster members Compute a biclique Qi(Xi, Yi) (complete subgraph) of Gi(Ai, Bi, Ei) Loukas Lazos, SECON 2009, University of Arizona

15. Problem: Biclique Construction • Design Criteria • Maximum edge biclique problem: Maximize the number of edges in Qi • Provides a balance between cluster size and # of common channels • Known to be NP-Complete [Peeters 2003] • Weighted maximum edge biclique problem: Maximize the weighted sum • Takes into account the quality of each channel • Also NP-Complete [Dawande et. al. 2001] • Constrained maximum edge biclique problem: Maximize edges subject to a constraint on the cardinality of one or both sets Xi, Yi Loukas Lazos, SECON 2009, University of Arizona

16. Heuristic for Maximum-Edge Biclique Computation A D C B G H 1 2 3 4 5 6 10 Loukas Lazos, SECON 2009, University of Arizona

17. Distributed Clustering Algorithm • Spectrum-Opportunity Clustering (SOC) • CRs individually compute their cluster memberships by solving the maximum edge biclique problem (or a variant). • CRs broadcast the computed cluster membership information to their neighbors, and update cluster memberships according to a total biqlique ordering. New cluster information is rebroadcasted. • CRs compute the final cluster membership information and broadcast one more time to ensure consistency • Can show that • All CRs individually reach to an agreement with respect to clusters and common idle channels • At least one CR is within one-hop range of all others in the cluster – can serve as clusterhead (CH) Loukas Lazos, SECON 2009, University of Arizona

18. Control Channel Migration CRA CH CRB PR Loukas Lazos, SECON 2009, University of Arizona

19. Clustering Performance Loukas Lazos, SECON 2009, University of Arizona

20. Further Problems to be Considered Need for re-clustering under various PR activity models Required reclustering frequency Development of local repair algorithms to avoid global reclustering Heterogeneity in channel quality Bandwidth: in multi-channel systems control channel saturation affects performance Communication range: nodes are one-hop neighbors at one frequency but not at another Evaluation of the overall throughput and delay of a system with dynamic control channel allocation Loukas Lazos, SECON 2009, University of Arizona