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Energy Efficient Spectrum Sensing Techniques for Cognitive Radio Networks: A Survey

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Jayakrishna P. S., Greshma V., T. Sudha

Jayakrishna P S., Greshma V. and T Sudha. Energy Efficient Spectrum Sensing Techniques for Cognitive Radio Networks: A Survey. International Journal of Computer Applications 160(4):20-23, February 2017. BibTeX

	author = {Jayakrishna P. S. and Greshma V. and T. Sudha},
	title = {Energy Efficient Spectrum Sensing Techniques for Cognitive Radio Networks: A Survey},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2017},
	volume = {160},
	number = {4},
	month = {Feb},
	year = {2017},
	issn = {0975-8887},
	pages = {20-23},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2017913033},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Cognitive radio has emerged as a tempting solution for the spectrum scarcity problem. This article focuses on the recent trends in energy efficient spectrum sensing techniques for the Cognitive Radio (CR) technology. The increasing demand of cognitive radio and its application increases the urge to make the emerging technologies as energy efficient as possible. Spectrum sensing which is one of the most complex and power intensive tasks in a cognitive radio system when made energy efficient increases the longevity of the network. This survey focuses on the new and efficient energy aware sensing techniques for cognitive radio networks and compares them.


  1. radios more personal”, IEEE Personal Commun. Mag., vol. 6, no. 4,pp. 13–18, Aug. 1999.
  2. Nabil Giweli, SeyedShahrestani Mitola, J. and J. Maguire, G. Q., “Cognitive radio: making software and Hon Cheung; “Spectrum sensing in cognitiveradio networks :Qosconsiderations”,CS IT-CSCP 2015 ,pp. 09-19.
  3. SinaMaleki, “Energy-Efficient Spectrum Sensing for Cognitive Radio Networks”, 2015 ,CSIT CSCP,pp 09-19
  4. Erik Axell, Geert Leus, Erik G. Larsson, and H. Vincent Poor, “Spectrum Sensing forCognitive Radio”; IEEE Signal Processing Magazine,May2012.
  5. Chia-hanLee,Wayne Wolf, “Energy Efficient Techniques for Cooperative SpectrumSensing in Cognitive Radios”, IEEE CCNC 2008 proceedings, pp 128-134.
  6. ViswanathanRamachandran, AlicCheeran, “Improvement of Energy Efficiency ofSpectrum Sensing Algorithms for Cognitive Radio Networks using Compressive SensingTechnique”,International Conference on Computer Communication and Informatics (ICCCI -2014), Jan, 2014,pp 1-6.
  7. Jan Oksanen, JarmoLunde, VisaKoivunen, “Reinforcement learningbasedsensing policyoptimization for energy efficient cognitive radio networks”, 2011 ElsevierJ. Oksanenetal./Neurocomputing80(2012), pp102 -110.
  8. Tazeen S. Syed, Ghazanfar A. Safdar, “History assisted Energy Efficient Spectrum Sensing for Infrastructure based Cognitive Radio Networks”, IEEE Transactions on vehicular technology, January 2016,pp 1-15.
  9. Muhammad Usman, Member, IEEE, DangsooHar, Senior Member, IEEE, and InsooKoo, Member, “IEEE,Energy-Efficient Infrastructure Sensor Network for Ad HocCognitive Radio Network”, IEEE Sensors Journal, January 2016, 1-13
  10. Zesheng Chen, Chao Chen., “Adaptive energy efficient spectrum probing in cognitiveradio networks” Elsevier Z. Chen, C. ChenAd Hoc Networks (2014) ,pp.256-270
  11. S. Ali Mousavifar and Cyril Leung, “Trust-Based Energy Efficient Spectrum Sensingin Cognitive Radio Networks”,IEEE journal2013, pp 1-6.
  12. Yi Liu, Rong Yu, Shengli Xie,Yan Zhang, and Victor C.M. Leung, “Energy-Efficient Spectrum Discovery for Cognitive Radio Green Networks”, 18th International conference on Telecommunication, ,pp 64 to 74, IEEE 2011.


Cognitive radio, Spectrum sensing, Energy efficient sensing, Censoring, Sleeping, Sequential detection, Confidence voting, Cluster collect forwarding, Compressive sensing, RL based sensing, History assisted sensing, WSN assisted sensing, Trust based sensing.