ENERGY DETECTION BASED SPECTRUM SENSING IN COGNITIVE RADIO NETWORK

Authors

  • Thangalakshmi B

DOI:

https://doi.org/10.20894/IJMSR.117.005.001.004

Keywords:

cognitive radio; dynamic spectrum access; software-defined radio.

Abstract

Cognitive radio (CR) is the enabling system for sustaining dynamic spectrum admittance: the policy that addresses the spectrum shortage difficulty that is encountered in many countries. The spectrum sensing trouble has gained new features with cognitive radio networks. Radio spectrum is the most expensive reserve in wireless communication. The cognitive radio and cognitive based networking are transforming the fixed spectrum allocation based communication systems in to dynamic spectrum allocation. Cognitive radios are smart devices with ability to detect environmental situations and can change its factors according to the necessity to get the optimized performance at the individual nodes or at network level Thus, CR is widely regarded as one of the most talented technologies for future wireless communications.

Downloads

Download data is not yet available.

Author Biography

Thangalakshmi B

Under Graduate Department of ECE, New Prince Shri Bhavani College of Engg and Technology, Pallikaranai, Chennai.

References

[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.

Downloads

Published

2013-12-17

Issue

Section

Articles