Development and Demonstration of Graphical User Interface Spectrum Sensing Algorithm using some Wireless Systems in South Africa

  • Jide Julius Popoola Department of Electrical and Electronics Engineering, Federal University of Technology, Akure, Nigeria
  • Rex van Olst School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa.
Keywords: Radio Spectrum, Dynamic Radio Access, Cognitive Radio, Spectrum Sensing, Spectrum Sensing and Detection Algorithm.

Abstract

The wireless communication industry using radio spectrum is recently going through major innovations and advancements. With this transformation, the demand for and usage of radio spectrum has increased exponentially making radio spectrum indeed a scarce natural resource. In order to solve this problem, the possibility of opening up the unused portions of licensed spectrum by sharing using cognitive radio technology has been in the spotlight for maximizing radio spectrum utilization as well to as ensure sufficient radio spectrum availability for future wireless services and applications. With this objective in mind, this paper looks at the principles and technologies of cooperative spectrum sensing in cognitive radio environment in improving radio spectrum utilization. The paper provides a comprehensive review on spectrum sensing as a key functional requirement for cognitive radio technology by focusing on its application on dynamic spectrum access that enables unused portions of licensed spectrum to be used in an opportunistic manner as long as the operation of the unlicensed user will not affect that of the licensed user. In satisfying this dynamic spectrum access requirement, a friendly interactive graphical user interface (GUI) spectrum sensing application program was developed. The detail activities involve in the development of the application program, also known as spectrum sensing and detection algorithm (SSADA), was fully documented and presented in the paper. The developed graphical user interface application program after successfully developed was evaluated. The performance evaluations of developed graphical user interface sensing algorithm show that the algorithm performs favourably well. The program overall evaluation results provide bedrock information on how to improve cooperative spectrum sensing gain without incurring a cooperative overhead.

References

Cave, M., Foster, A. and Jones, R. W. (2006). Radio Spectrum Management: Overview and Trends, Proc. of ITU Spectrum Workshop 2006, pp. 1-22, September 2006, Online [Available]: http://www.itu.int/osg/spu/stn/spectrum/workshop_proceedings/Background_Papers_Final/Adrian%20Foster%20-%20CONCEPT_PAPER_20_9_06_Final.pdf. Accessed on 4 November 2008.

Nunno, R. M. (2002). Review of Spectrum Management Practices. Fed. Comm. Commission Int. Bureau Strategic Analysis and Negotiations Division, pp. 1-15. Online [Available]: http://www.ictregulationtoolkit.org/en/Document.2270.pdf. Accessed on 16 August 2008.

Olafsson, S., Glover, B. and Nekovee, M. (2007). Future Management of Spectrum. BT Technology Journal, Vol. 25, No. 2, pp. 52-63.

https://doi.org/10.1007/s10550-007-0028-2

Akyildiz, I. F., Lee, W. Y., Vuran, M. C. and Mohanty, S. (2007). Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A Survey. Computer Networks Journal, Vol. 50, No. 13, pp. 2127-2159.

https://doi.org/10.1016/j.comnet.2006.05.001

Haykin, S. (2005). Cognitive Radio: Brain-Empowered wireless Communications. IEEE Journal on Selected Areas in Communications, Vol. 23, No. 2, pp. 201-220.

https://doi.org/10.1109/JSAC.2004.839380

Scutari, G., Palomar, D. P., and Barbarossa, S. (2008). Cognitive MIMO Radio Competitive Optimality Design Based on Subspace Projections. IEEE Signal Processing Magazine, pp. 46-59.

https://doi.org/10.1109/MSP.2008.929297

FCC (2002). Spectrum Policy Task Force Report. FCC Document ET Docket No. 02-135, pp.1-22. Online [Available]: http://transition.fcc.gov/sptf/files/E&UWGFinalReport.pdf. Accessed on 19 May 2008.

Song, Y. Fang, Y., and Zhang, Y. (2007), Stochastic Channel Selection in Cognitive Radio Networks. In IEEE Proc. of Global Communications Conference (GLOBECOM), Washington, DC, pp. 4878-4882.

https://doi.org/10.1109/GLOCOM.2007.925

Chen, R., Park, J-M., and Reed, J. H. (2008). Defense against Primary User Emulation Attacks in Cognitive Radio Networks. IEEE Journal on Selected Areas in Communications, Vol. 26, No. 1, pp. 25-37.

https://doi.org/10.1109/JSAC.2008.080104

Čabrić, D., Mishra, S. M., Willkomm, D., Brodersen, R., and Wolisz, A. (2005). A Cognitive Radio Approach for Usage of Virtual Unlicensed Spectrum. In Proc. of 14th 1st Mobile Wireless Communications Summit. Online [Available]: http://www.eurasip.org/Proceedings/Ext/IST05/papers/411.pdf. Accessed on 26 January 2015.

Chakravarthy, V., Nunez, A. S., and Stephens, J. P. (2005). TDCS, OFDM, and MC-CDMA: A Brief Tutorial. IEEE Radio Communications, pp. S11-S16.

https://doi.org/10.1109/MCOM.2005.1509966

Čabrić, D., and Brodersen, R. W. (2005). Physical Layer Design Issues Unique To Cognitive Radio Systems. In Proceedings of 16th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2005), Berlin, 759-763.

https://doi.org/10.1109/PIMRC.2005.1651545

Čabrić, D., Tkachenko, A., and Brodersen, R. W. (2006). Spectrum Sensing Measurements of Pilot, Energy, and Collaborative Detection. In Proc. of IEEE Military Communications Conference (MILCOM), Washington, DC, USA, pp. 1-7.

https://doi.org/10.1109/MILCOM.2006.301994

Gandetto, M., and Regazzoni, C. (2007). Spectrum Sensing: A Distributed Approach for Cognitive Terminals. IEEE Journal on Selected Areas in Communications, Vol. 25, No. 3, pp. 546-557.

https://doi.org/10.1109/JSAC.2007.070405

Larsson, E. G., and Regnoli, G. (2007). Primary System Detection for Cognitive Radio: Does Small-Scale Fading Help? IEEE Communication Letters, Vol. 11, No. 10, pp. 799-801.

https://doi.org/10.1109/LCOMM.2007.070923

Popoola, J. J., and van Olst, R. (2011). Cooperative Sensing Reliability Improvement for Primary Radio Signal Detection in Cognitive Radio Environment. In Proc. of Southern Africa Telecommunication Networks and Applications Conf. (SATNAC), East London, South Africa, pp. 131- 136.

Lee, C.-H., and Wolf, W. (2008). Energy Efficient Techniques for Cooperative Spectrum Sensing in Cognitive Radios. In Proc. of 5th IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, pp. 968-972.

https://doi.org/10.1109/ccnc08.2007.223

Akyildiz, I. F., Lo, B. F., and Balakrishnan, R. (2011). Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey. Physical Communication. Vol. 4, No. 1, pp. 40-62.

https://doi.org/10.1016/j.phycom.2010.12.003

Mishra, S. M., Sahai, A., and Brodersen, R. (2006). Cooperative Sensing among Cognitive Radios. In Proc. of IEEE Inter. Conf. on Communications (ICC), Istanbul, pp. 1658-1663.

https://doi.org/10.1109/ICC.2006.254957

Popoola, J. J., and van Olst, R. (2013). The Performance Evaluation of a Spectrum Sensing Implementation using an Automatic Modulation Classification detection Method with a Universal Software Radio Peripheral. An International Journal on Expert Systems with Applications, Vol. 40, No. 6, pp. 2165-2173.

https://doi.org/10.1016/j.eswa.2012.10.047

Neihart, N. M., Roy, S., and Allstot, D. J. (2007). A Parallel Muilt-Resolution Sensing Technique for Multiple Antenna Cognitive Radios. In Proc. of IEEE Inter. Symp. on Circuits and Systems (ISCAS), New Orleans, pp. 2530-2533.

https://doi.org/10.1109/ISCAS.2007.378754

Published
2015-09-30
How to Cite
Popoola, J. J., & Olst, R. van. (2015). Development and Demonstration of Graphical User Interface Spectrum Sensing Algorithm using some Wireless Systems in South Africa. Journal of Applied Science & Process Engineering, 2(2), 44-63. https://doi.org/10.33736/jaspe.164.2015