Kitsune : A Management System for Cognitive Radio Networks Based on Spectrum Sensing

1. Kitsune: A Management System for Cognitive Radio Networks Based on Spectrum Sensing Lucas Bondan Federal University of Rio Grande do Sul (UFRGS) IEEE/IFIP NOMS 2014 5 – 9 May, 2014 Krakow – Poland

2. Outline Introduction Background Proposed solution Experimental evaluation Final remarks

3. Introduction • Crescent number of devices are using Radio Frequency (RF) spectrum for communication • However, this resource is limited • Command and Control (CaC) policy causes underutilization[FCC, 2002] • Only licensed users can transmit in licensed frequencies • Rise of Cognitive Radio (CR) concept [Mitola and Maguire, 1999] • Explored to improve the RF spectrum utilization • Cognitive capability • Reconfigurability

4. Introduction (cont.) • Rise of CR networks • Designed to operate opportunistically • IEEE 802.22 Standard • Base Station (BS) provides Internet access to Customer-Premise Equipment (CPE) • Question: • How the management of these networks may be provided?

5. Background CR Characteristics • Cognitive Capability • Cognitive Functions (CF)s: sensing, decision, sharing, and mobility • Spectrum sensing results used as input to the others • Reconfigurability • RF environment is dynamic in its nature • CR devices should be reconfigurable • Network administrator should know the RF environment

6. Objective Design and develop a management system for CR networks • Enables the network administrator to know the radio environment • Configuration, monitoring,and visualization of spectrum sensing function should be provided by the management system • A continuous learning process for the network administrator • IEEE 802.22 Standard assumes a management system

7. Proposed solution Kitsune • Hierarchical management system for CR networks • Considers CR networks characteristics, operating on the spectrum sensing function • Different networks can be managed using a hierarchical architecture • Management Information Base (MIB) based on IEEE 802.22 MIB • Information organization • Based on Resource Oriented Architecture (ROA) • Fast, simple, and robust

8. Proposed solution Components Network Operations Control (NOC) CR Network Network Administrator CPE Agent CF MIB Management Station BS Backhaul Gateway Manager … … Visualization Configuration CPE Cache Agent Monitoring CF MIB

9. Experimental evaluation Scenario

10. Experimental evaluation 1 - Channels Occupancy • 5 CPEs using 5 channels (one channel per CPE) • Important to evaluate the radio environment • How the channels are used by CPEs

11. Experimental evaluation 2 - Geolocation • Geolocation is an important factor in wireless networks • CPEs with low signal strength may be distant from the BS • 1 BS and 5 CPEs • Visualize the CR devices location and estimated coverage area

12. Experimental evaluation 3 - Transmissions • 5 CPEs, each one transmitting in one channel • Important to analyze the number of transmissions performed by each CPE in each channel

13. Experimental evaluation 4 - Uplink throughput • Complementary to the previous visualization • 5 CPEs, each one transmitting in one channel • Average throughput obtained in the transmissions • What channels present the highest/lowest throughput

14. Experimental evaluation 5 - Configuration • Reconfiguration of the sensing period • Interval between spectrum sensing executions • Observe the average throughput obtained Sensing period Throughput Variation Channel [s] [Mbps] [%] 1 0.3182 1 42.04 2 0.5490 1 0.2267 2 51.84 2 0.4708 1 0.4016 3 18.42 2 0.4923 1 0.1803 4 42.54 2 0.3138 1 0.4027 5 17.25 2 0.4867

15. Final remarks Conclusions • Main contribution: Hierarchical management system • Provides to the network administrator a way to analyze the network environment • Eases the analysis of the spectrum sensing results • A management system for CR networks should consider the spectrum sensing function • Visualizations may improve the network administrator knowledge • Configuration, monitoring, and visualization are part of a continuous process

16. Final remarks Future Work • Extend Kitsune operation for different networks architectures • Concepts of management by delegation may be explored • Extend Kitsune operation to cover all cognitive functions • Turn Kitsuneable toanalyze the best algorithm for each cognitive function

17. Thank you! Questions? Lucas Bondan Website: inf.ufrgs.br/~lbondan E-mail: lbondan@inf.ufrgs.br

18. Bibliography [FCC, 2002] Federal Communications Comission Spectrum Policy Task Force, “Report of the Spectrum Efficiency Working Group”, FCC, 2002 [Mitola e Miguire, 1999] I. Mitola, J. and J. Maguire, G.Q., “Cognitive radio: making software radios more personal,” IEEE Personal Communications, vol. 6, pp. 13–18, 1999 [IEEE, 2011] IEEE, “IEEE Standard for Information Technology - Telecommunicationsandinformationexchangebetween systems Wireless Regional AreaNetworks (WRAN) - SpecificrequirementsPart 22: Cognitive Wireless RAN Medium Access Control (MAC) andPhysicalLayer (PHY) Specifications: Policies and Procedures for Operation in theTV Bands,” IEEE Std 802.22, pp. 1–680, 2011. [Akyildizet al., 2006] Akyildiz, I. F., Lee, W.-Y., Vuran, M. C., andMohanty, S. 2006. Next generation/dynamicspectrumaccess/cognitive radio wireless networks: A survey. Computer Networks 50, 13, 2127 – 2159. [Akyildiz; Lee; Chowdhury] Akyildiz, I. F.; Lee, W.-Y.; Chowdhury, K. R. CRAHNs: cognitive radio ad hoc networks. Ad Hoc Networks, Amsterdam, The Netherlands, v.7, n.5, p.810–836, July 2009. [CHEN et al., 2007] Chen, T.; Zhang, H.; Maggio, G. M.; Chlamtac, I. CogMesh: a cluster-based cognitive radio network. IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), p.168–178, Apr. 2007.

19. Bibliography (cont.) [PotierandQuian, 2011] P. Potierand L. Qian, “Network management ofcognitive radio ad hoc networks,” InternationalConferenceonCognitive Radio andAdvanced Spectrum Management, pp. 1–5, 2011. [Wang et al., 2008] C.-X. Wang, H.-H. Chen, X. Hong, and M. Guizani, “Cognitive radio network management,” IEEE Vehicular Technology Magazine, pp. 28–35, 2008. [Manfrin; Zanella; Zorzi]Manfrin, R.; Zanella, A.; Zorzi, M. CRABSS: calradio-basedadvancedspectrum scanner for cognitive networks. Wireless Communication & Mobile Computing, Chichester, UK, v.10, n.12, p.1682–1695, 2010. [Stavroulakiet al., 2012] V. Stavroulaki, A. Bantouna, Y. Kritikou, K. Tsagkaris, P. Demestichas, P. Blasco, F. Bader, M. Dohler, D. Denkovski, V. Atanasovski, L. Gavrilovska, and K. Moessner, “Knowledge Management Toolbox: Machine Learning for Cognitive Radio Networks,” IEEE Vehicular Technology Magazine, pp. 91–99, june2012 [Yucek e Arslan] T. Yucekand H. Arslan, “A surveyofspectrumsensingalgorithmsfor cognitiveradio applications,” IEEE Communications SurveysTutorials, vol. 11, pp. 116–130, 2009. [Bondan et al., 2013] Bondan, L.; Kist, M.; Kunst, R.; Both, C.; Rochol, J.; Granville, L.. ”Uma Solução para Gerenciamento de Dispositivos de Rádio Cognitivo Baseada na MIB IEEE 802.22”. Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), 2013, Brasília - Brazil

20. Introduction (cont.) • Rise of CR networks • Designed to operate opportunistically • IEEE 802.22 Standard • Related Work • Specific cognitive functions are addressed • Highlights the importance of management solutions • No management system for CR networks was proposed • Objective • Design a management system for CR networks

21. Experimental evaluation RSSI • 5 CPEs using 5 channel (one per CPE) • Importantto observe thesignalsquality. • UsingtheEnergy Detectiontechnique, a high RSSI indicatesanoccupiedchannel.

22. Introduction (cont.) • Proposed Solution • Management system for CR networks considering the spectrum sensing function • Configuration • Monitoring • Visualization • Provides a continuous learning process to the network administrator