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Kitsune: A Management System for Cognitive Radio Networks Based on Spectrum Sensing
Lucas Bondan
IEEE/IFIP NOMS 20145 – 9 May, 2014Krakow – Poland
Federal University of Rio Grande do Sul (UFRGS)
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Outline
• Introduction
• Background
• Proposed solution
• Experimental evaluation
• Final remarks
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Introduction• Crescent number of devices are using Radio
Frequency (RF) spectrum for communicationo However, this resource is limitedo 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] o Explored to improve the RF spectrum utilization
Cognitive capability Reconfigurability
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Introduction (cont.)
• Rise of CR networkso Designed to operate opportunisticallyo IEEE 802.22 Standard
Base Station (BS) provides Internet access to Customer-Premise Equipment (CPE)
• Question:o How the management of these networks may be provided?
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CR Characteristics
• Cognitive Capabilityo Cognitive Functions (CF)s: sensing, decision, sharing, and
mobilityo Spectrum sensing results used as input to the others
• Reconfigurabilityo RF environment is dynamic in its natureo CR devices should be reconfigurableo Network administrator should know the RF environment
Background
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ObjectiveDesign and develop a management system
for CR networks
o Enables the network administrator to know the radio environment
o Configuration, monitoring, and visualization of spectrum sensing function should be provided by the management system A continuous learning process for the network
administratoro IEEE 802.22 Standard assumes a management system
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Kitsune• Hierarchical management
system for CR networkso Considers CR networks characteristics,
operating on the spectrum sensing function
o Different networks can be managed using a hierarchical architecture
o Management Information Base (MIB) based on IEEE 802.22 MIB Information organization
o Based on Resource Oriented Architecture (ROA) Fast, simple, and robust
Proposed solution
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ComponentsNetwork Operations Control (NOC)
ManagementStation
Gateway
Cache
Agent
MIB
CF
BS
CPE
CR Network
NetworkAdministrator
Manager
Configuration
Monitoring
Visualization
…
Agent
MIB
CF
CPE
Backhaul …
Information Flow Functional Flow
Proposed solution
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Scenario
Parameter Value
Execution time per experiment 60 s
Number of channels 5
Number of CPEs 5
Poisson Mean and Variance (λ) [1 - 5] s
Sensing Duration [0.1] s
Sensing Period [1, 2] s
Maximum bandwidth per channel 6 MHz
Manager Periodicity (Pm) 30 s
Gateway Periodicity (Pg) 2 s
CPE CPE
CPECPE
ManagementStation
BS
Experimental evaluation
CPE
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1 - Channels Occupancy
Experimental evaluation
• 5 CPEs using 5 channels (one channel per CPE)
• Important to evaluate the radio environmento How the channels are used by CPEs
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2 - Geolocation
Experimental evaluation
• Geolocation is an important factor in wireless networkso 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
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3 - Transmissions
Experimental evaluation
• 5 CPEs, each one transmitting in one channel
• Important to analyze the number of transmissions performed by each CPE in each channel
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4 - Uplink throughput
Experimental evaluation
• Complementary to the previous visualization
• 5 CPEs, each one transmitting in one channel
• Average throughput obtained in the transmissionso What channels present the highest/lowest throughput
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5 - Configuration
Experimental evaluation
ChannelSensing period
[s]Throughput
[Mbps]Variation
[%]
11 0.3182
42.042 0.5490
21 0.2267
51.842 0.4708
31 0.4016
18.422 0.4923
41 0.1803
42.542 0.3138
51 0.4027
17.252 0.4867
• Reconfiguration of the sensing periodo Interval between spectrum sensing executions
• Observe the average throughput obtained
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Conclusions
• Main contribution: Hierarchical management systemo Provides to the network administrator a way to analyze the
network environmento Eases the analysis of the spectrum sensing results
• A management system for CR networks should consider the spectrum sensing functiono Visualizations may improve the network administrator knowledge
• Configuration, monitoring, and visualization are part of a continuous process
Final remarks
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Future Work
• Extend Kitsune operation for different networks architectureso Concepts of management by delegation may be explored
• Extend Kitsune operation to cover all cognitive functions
• Turn Kitsune able to analyze the best algorithm for each cognitive function
Final remarks
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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 - Telecommunications and information exchange between systems Wireless Regional Area Networks (WRAN) - Specific requirements Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Policies and Procedures for Operation in the TV Bands,” IEEE Std 802.22, pp. 1–680, 2011.
[Akyildiz et al., 2006] Akyildiz, I. F., Lee, W.-Y., Vuran, M. C., and Mohanty, S. 2006. Next generation/dynamic spectrum access/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.
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Bibliography (cont.)[Potier and Quian, 2011] P. Potier and L. Qian, “Network management of cognitive radio ad hoc networks,” International Conference on Cognitive Radio and Advanced 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-based advanced spectrum scanner for cognitive networks. Wireless Communication & Mobile Computing, Chichester, UK, v.10, n.12, p.1682–1695, 2010.
[Stavroulaki et 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, june 2012
[Yucek e Arslan] T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys Tutorials, 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
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Introduction (cont.)
• Rise of CR networkso Designed to operate opportunisticallyo IEEE 802.22 Standard
• Related Worko Specific cognitive functions are addressedo Highlights the importance of management solutionso No management system for CR networks was proposed
• Objectiveo Design a management system for CR networks
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RSSI
Experimental evaluation
• 5 CPEs using 5 channel (one per CPE)
• Important to observe the signals quality.o Using the Energy Detection technique, a high RSSI
indicates an occupied channel.
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Introduction (cont.)
• Proposed Solutiono Management system for CR networks considering the spectrum
sensing function Configuration Monitoring Visualization
o Provides a continuous learning process to the network administrator
