Presented By Saifur Rahman Sabuj A Theoretical Framework for Evaluation of Random Cognitive Radio Network Presented By Saifur Rahman Sabuj Supervisor Masanori Hamamura Kochi University of Technology, Japan

Agenda Motivation Downlink Model of CR Network Network Model Time-slot Structure Outage probability Energy Efficiency Transmit Antenna Selection Analysis of Rayleigh-lognormal Uplink Model of CR Network Coverage Probability Spectral Efficiency Energy Harvesting of CR Network Conclusion and Future Work Kochi University of Technology

Motivation Proposed 1 Proposed 2 Before 2011 After 2011 Analog TV broadcasting 90 - 222 MHz (1 - 12 ch) Analog and Digital TV broadcasting 470 - 770 MHz (13 - 62 ch) Before 2011 Digital TV broadcasting 470 - 770 MHz (13 - 62 ch) After 2011 Proposed 1 Proposed 2 470 - 770 MHz (13 - 62 ch) 802.22 802.11 af 470 - 770 MHz (13 - 62 ch) Digital TV Broadcasting ITS Cellular System Please See Reference No. 1, 2, 3, 4, 5 and 6 for more information.

Motivation Macro-BS Micro-BS Femto-BS Pico-BS Relay WLAN Base Station

Motivation Voronoi Diagram Macro Base Station Micro Base Station Pico Base Station Please See Reference No. 7, 8 and 9 for more information.

CR Terminology Primary Users (PUs): who hold a license for a specific portion of frequency band. Cognitive or Secondary Users (CUs or SUs): who access a licensed frequency band without interference of PUs. Primary Networks (PNs): Operate/access their own (license) frequency band. e.g., TV broadcast or cellular system. Cognitive or Secondary Networks (CNs or SNs): Operate/access a licensed frequency when PUs are not utilized the frequency. Interweave Scheme: CUs can utilize the frequency when PUs are not transmitting. Underlay Scheme: CUs can access the frequency while PUs are active in transmission.

Performance Metric Coverage Probability: A message is successfully decoded when the signal-to-interference-plus-noise ratio (SINR) of useful signal is greater than a particular threshold. The coverage probability is the probability that the SINR is greater than the SINR threshold. Outage Probability: The outage probability is the probability that the SINR is lower than the SINR threshold. Spectral Efficiency: The spectral efficiency is the probability that the Shannon capacity on the user link is greater than a particular threshold. Energy Efficiency: The energy efficiency can be defined as the ratio between area spectral efficiency and average network power consumption. Please See Reference No. 8, 9, 11, and 14 for more information.

Proposed CR Network Model Primary Transmitter Cognitive Macro Base Station

Downlink CR Network Model Cognitive Macro BS Cognitive/Secondary User Primary Transmitter Primary User

CR Network Model Primary User Cognitive Macro BS Cognitive/Secondary User Primary Transmitter

Kochi University of Technology Time-slot Structure PU ch1 ch2 ch3 ch4 ch9 chN ch1 ch2 chN ch2 ch5 chN ch7 SU1 ch3 ch4 ch9 ch3 ch6 ch8 ch7 SU2 SU ch5 ch6 ch8 ch6 ch5 ch9 ch1 SU N T T s t T occupied unoccupied success collision Please See Reference No. 10 for more information. Kochi University of Technology

Outage Probability Outage Probability of PR Outage Probability of SR

Outage Probability Outage Probability of PR

Outage Probability Outage Probability of SR (a) Perfect detection of PTs and STs (b) Perfect detection of PTs and imperfect detection of STs

Outage Probability Outage Probability of SR (c) Imperfect detection of PTs and perfect detection of STs (d) Imperfect detection of PTs and STs

Energy Efficiency Energy Efficiency of PN Energy Efficiency of CN/SN (a) Perfect detection of PTs and STs (b) Perfect detection of PTs and imperfect detection of STs (c) Imperfect detection of PTs and perfect detection of STs (d) Imperfect detection of PTs and STs

Transmit Antenna Selection Minimum transmit power for a particular outage probability Energy Efficiency for the Nt transmit antenna

Simulation Parameters Value N 25 α 4 λp 15/km M 10 μp 0.6 λp' 13/km Ns 15 μc 0.3 λs 50/km Ts 1ms Pp 50W λs' 45/km Tp 4ms Ps 15W NTRX 1 ∆s 2.8 Po 84W Pss 0.2W β Pd 0.9 Pf 0.1 2 2 2 2 Kochi University of Technology

Outage Probability Kochi University of Technology

Outage Probability Kochi University of Technology

Energy Efficiency Kochi University of Technology

Energy Efficiency Kochi University of Technology

Transmit Antenna Selection Kochi University of Technology

Comparison between Previous and Current Work S. R. Sabuj and M. Hamamura, “Energy Efficiency Analysis of Cognitive Radio Network using Stochastic Geometry” in Proc. IEEE CSCN, pp. 245-251, Oct. 2015. S. R. Sabuj and M. Hamamura, “Outage and Energy-Efficiency Analysis of Cognitive Radio Networks: A Stochastic Approach to Transmit Antenna Selection” Pervasive and Mobile Computing, Elsevier, 2017. Kochi University of Technology

Comparison between Previous and Current Work S. R. Sabuj and M. Hamamura, “Energy Efficiency Analysis of Cognitive Radio Network using Stochastic Geometry” in Proc. IEEE CSCN, pp. 245-251, Oct. 2015. S. R. Sabuj and M. Hamamura, “Outage and Energy-Efficiency Analysis of Cognitive Radio Networks: A Stochastic Approach to Transmit Antenna Selection” Pervasive and Mobile Computing, Elsevier, 2017. Kochi University of Technology

Uplink CR Network Model Cognitive Macro BS Cognitive/Secondary User Primary Transmitter Primary User

Uplink CR Network Model (b) (a) Cognitive Macro BS Cognitive/Secondary User Primary Transmitter Primary User

Analytical Expression of Uplink Model Coverage Probability (a) Perfect detection of PTs and STs (b) Perfect detection of PTs and imperfect detection of STs

Analytical Expression of Uplink Model Coverage Probability (c) Imperfect detection of PTs and perfect detection of STs (d) Imperfect detection of PTs and STs

Coverage Probability

Coverage Probability

Spectral Efficiency

Spectral Efficiency

Conclusion Downlink Model: Uplink Model: Outage probability is reduced for decreasing number of PTs and STs. The SNIR threshold impacts on energy efficiency performance, and there exists an optimal threshold that maximizes energy efficiency. Uplink Model: Coverage probability and spectral efficiency are better when the SR/CR is located outside the PER. Coverage probability and spectral efficiency depend on the path-loss exponent and SR/CR density.

Random Cognitive Radio Network: The Need for Future Wireless Future direction: Multi-tier Optimization Relay and beamforming Device-to-Device Percolation theory Random Cognitive Radio Network: The Need for Future Wireless

Publication Conference Papers: Journal Papers: S. R. Sabuj and M. Hamamura, “Random Cognitive Radio Network Performance in Rayleigh-Lognormal Environment” in Proc. IEEE CCNC, pp. 999-1004, Jan. 2017. S. R. Sabuj and M. Hamamura, “Energy Efficiency Analysis of Cognitive Radio Network using Stochastic Geometry” in Proc. IEEE CSCN, pp. 245-251, Oct. 2015. S. R. Sabuj M. Hamamura, and S. Kuwamura “Detection of Intelligent Malicious User in Cognitive Radio Network by Using Friend or Foe (FoF) Detection Technique” in Proc. ITNAC, pp. 155-160, Nov. 2015. (Session best paper award) S. R. Sabuj and M. Hamamura, “Approaches to the Dynamic Efficiency in Wireless Communications” in Proc. ISCIT, pp. 453-457, Sept. 2014. Journal Papers: S. R. Sabuj and M. Hamamura, “Outage and Energy-Efficiency Analysis of Cognitive Radio Networks: A Stochastic Approach to Transmit Antenna Selection” Pervasive and Mobile Computing, Elsevier (Accepted). IF: 2.34 S. R. Sabuj and M. Hamamura, “Uplink Modeling of Cognitive Radio Network Using Stochastic Geometry” Performance Evaluation, Elsevier (under review). IF: 1.613 S. R. Sabuj and M. Hamamura, “Two-slope Path-loss Design of Energy Harvesting in Random Cognitive Radio Networks” Computer Networks, Elsevier (under review). IF: 2.516 S. R. Sabuj and M. Hamamura, “Signal Technique for Friend or Foe Detection of Intelligent Malicious User in Cognitive Radio Network” International Journal of Ad Hoc and Ubiquitous Computing, Inderscience (under review). IF: 0.705 Kochi University of Technology

References Kochi University of Technology S. Ohmori, “Report on Japan Study in Cognitive Radio and Networks," 2011. -------------, “NICT news," vol. 7, no. 394, July 2013. H. Murakami and H. Harada, “Cognitive Radio based Spectrum Sharing in the Television Broadcast Bands," Smart Wireless Laboratory, NICT, 2013. [Online]. Available: http://www.nict.go.jp/en/press/2014/01/23-1.html, collected: 05 May, 2016. [Online]. Available: http://www2.nict.go.jp/wireless/smartlab/project/astra sensing.html, collected: 05 May, 2016. [Online]. Available: http://www2.nict.go.jp/wireless/smartlab/project/pbb.html, collected: 05 May, 2016. A. D. Wyner, “Shannon-theoretic approach to a gaussian cellular multiple-access channel," IEEE Trans. Inf. Theory, vol. 40, no. 6, pp. 1713-1727, 1994. A. Guo and M. Haenggi, “Spatial stochastic models and metrics for the structure of base stations in cellular networks," IEEE Trans. Wireless Commun., vol. 12, no. 11, pp. 5800-5812, Nov. 2013. M. Haenggi, Stochastic Geometry for Wireless Networks, Cambridge University Press, 2012. X. Li, H. Liu, S. Roy, J. Zhang, P. Zhang, C. Ghosh, “Throughput analysis for a multi-user, multi-channel ALOHA cognitive radio system," IEEE Trans. Wireless Commun., vol. 11, no. 11, pp. 3900-3909, Nov. 2012. C. Li, J. Zhang, and K. B. Letaief, “Throughput and energy efficiency analysis of small cell networks with multi-antenna base stations," IEEE Trans. Wireless Commun., vol. 13, no. 5, pp. 2505-2517, May 2014. M. T. Kakitani, G. Brante, R. D. Souza, and M. A. Imran, “Energy efficiency of transmit diversity systems under a realistic power consumption model," IEEE Commun. Lett., vol. 17, no. 1, pp. 119122, Jan. 2013. L. Li, X. Zhou, H. Xu, G. Y. Li, D. Wang, and A. Soong, “Energy-efficient transmission in cognitive radio networks," in Proc. IEEE CCNC, pp. 1-5, 2010. J. Andrews, F. Baccelli, and R. K. Ganti, “A tractable approach to coverage and rate in cellular networks," IEEE Trans. Commun., vol. 59, no. 11, pp. 3122-3134, Nov. 2011. C. -H. Lee and M. Haenggi, “Interference and outage in poisson cognitive networks," IEEE Trans. Wireless Commun., vol. 11, no. 4, pp. 1392-1401, April 2012. Kochi University of Technology

References Kochi University of Technology Federal Communications Commission, “Spectrum policy task force report,” FCC 02-155, Nov. 2002. J. Mitola III, “Cognitive radio an integrated agent architecture for software defined radio,” Ph.D. dissertation, KTH, Sweden, Dec. 2000. S. Haykin, “Cognitive radio: Brain-empowered wireless communications, IEEE J. Sel. Areas Commun., vol. 23, pp. 201-220, Feb. 2005. E. Hossain, D. Niyato and Z. Han, Dynamic Spectrum Access and Management in Cognitive Radio Networks, Cambridge University Press, 2009. I. F. Akyildiz, W.-Y. Lee, M. C. Vuran and S. Mohanty, “A Survey on spectrum management in cognitive radio networks,” IEEE Commun. Mag., vol. 46, Iss. 4, pp. 40-48, 2008. [Online]. Available: https://en.wikipedia.org/wiki/Stochastic_geometry_models_of_wireless_networks. Kochi University of Technology

Through the global, we are connected to each other by mobile phone Thank you for your kind attention Question ? Through the global, we are connected to each other by mobile phone