• Thangalakshmi
  • Bharathy G T



Spectrum Sensing, Cognitive Radio, Energy Detection and Matched Filter Detection.


In current day wireless communication has become the most popular communication. Because of this growing demand on wireless applications has put a lot of constraints on the available radio spectrum which is incomplete and expensive. In permanent spectrum assignments there are many frequencies that are not being accurately used. So cognitive radio helps us to use these idle frequency bands which are also called as White Spaces. This is an exceptional approach to improve exploitation of radio electromagnetic spectrum. In Establishing the cognitive radio there are four important methods. In this paper we are going to discuss about the first and most important method to implement cognitive radio i.e., spectrum sensing. The challenges, issues and techniques that are involved in spectrum sensing will discussed in detail.


Download data is not yet available.

Author Biographies


Post Graduate Department of ECE, Jerusalem College of Engineering, Pallikaranai, Chennai.

Bharathy G T

Assistant Professor of ECE, Jerusalem College of Engineering, Pallikaranai, Chennai.


[1] Haykin, S., Thomson, D., “Spectrum sensing for cognitive radio,” Proc. IEEE., Vol. 97, No. 5, pp. 849–877, 2009.

[2] Digham, F., Alouini, M., “On the energy detection of unknown signals over fading channels,” IEEE Tranms.Commun., Vol. 55, No. 1, pp. 21–24, 2007.

[3] Zeng, Y., Liang,Y., “Eigenvalue-based spectrum sensing algorithms for cognitive radio,” IEEE Trans. Commun., Vol. 57, No. 6, pp. 1784–1793, 2012 .

[4] Labeau, F., Kassouf, M., “A Markov-Middleton model for bursty impulsive noise: modeling and receiver design,” IEEE Trans. Power Delivery, Vol. 28, No. 4, pp. 2317–2325, 2013.

[5] Halverson, D., Wise, G., “Discrete-time detection in mixing noise,” IEEE Trans. Inf. Theory, Vol. 26, No. 2, pp. 189–198, 1980.

[6] Thomas, J., “Memoryless discrete-time detection of a constant signal in m-dependent noise,” IEEE Trans. Inf. Theory, Vol. 25, No. 1, pp. 54–61 , 2013.

[7] Maras, A., “Locally optimum detection in moving average non-gaussian noise,” IEEE Trans. Commun., Vol. 36, No. 8, pp. 907–912, 2013.

[8] Kim, T., Yun, J., “Comparison of known signal detection schemes under a weakly dependent noise model,” in IEEE Proceedings–Vision, Image and Signal Processing, Vol. 141, No. 5, pp. 303–310, 1994.

[9] Poor, H., “Signal detection in the presence of weakly dependent noise–I: optimum detection,” IEEE Trans. Inf. Theory, Vol. 28, No.5, pp. 735–744, 1982.

[10] Moghimi, F., Nasri, A., “Adaptive L p–norm spectrum sensing for cognitive radio networks,” IEEE Trans. Commun., Vol. 99, pp. 1–12, 2011.