Hybrid Spectrum Sensing Algorithm for Cognitive Radio Network

sensing plays a very provocative role in cognitive radio network. In order to utilize spectrum more efficiently and to exploit the primary user, spectrum sensing is accomplished. We proposed a new hybrid algorithm for detection of primary user in cognitive radio network. The theoretical analysis and simulation is also presented in this paper. This research work includes an analogy with Energy Based Detection and Cyclostationary Feature Detection. Our proposed algorithm is a flexible algorithm, the Cyclostationary feature algorithm act as feature extractor when primary user is present and function as detector when primary user is absent. The results show that it is optimum spectrum sensing algorithm under different SNR values. It has removed the shortcomings faced by both sensing algorithms i.e. Energy Based Detection and Cyclostationary Feature Detection.

[1]  R. Tandra,et al.  Fundamental limits on detection in low SNR under noise uncertainty , 2005, 2005 International Conference on Wireless Networks, Communications and Mobile Computing.

[2]  W. Gardner.  Exploitation of spectral redundancy in cyclostationary signals , 1991, IEEE Signal Processing Magazine.

[3]  Markku J. Juntti,et al.  Threshold setting strategies for a quantized total power radiometer , 2005, IEEE Signal Processing Letters.

[4]  Lars Berlemann.  Cognitive Radio and Management of Spectrum and Radio Resources in Reconfigurable Networks , 2005 .

[5]  Miguel Soriano,et al.  Securing cognitive radio networks , 2010 .

[6]  Anant Sahai,et al.  Some Fundamental Limits on Cognitive Radio , 2004 .

[7]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[8]  A. Ghasemi,et al.  Collaborative spectrum sensing for opportunistic access in fading environments , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[9]  Geoffrey Ye Li,et al.  Agility improvement through cooperative diversity in cognitive radio , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[10]  Qusay H. Mahmoud,et al.  Cognitive Networks: Towards Self-Aware Networks , 2007 .

[11]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[12]  R.W. Brodersen,et al.  Cyclostationary Feature Detector Experiments Using Reconfigurable BEE2 , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[13]  Danijela Cabric,et al.  Physical layer design issues unique to cognitive radio systems , 2005, 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications.

[14]  Hüseyin Arslan,et al.  A survey of spectrum sensing algorithms for cognitive radio applications , 2009, IEEE Communications Surveys & Tutorials.

[15]  Mohamed-Slim Alouini,et al.  On the Energy Detection of Unknown Signals Over Fading Channels , 2007, IEEE Transactions on Communications.

[16]  J. I. Mararm,et al.  Energy Detection of Unknown Deterministic Signals , 2022 .

[17]  A. Lackpour,et al.  A scalable dynamic spectrum allocation system with interference mitigation for teams of spectrally agile software defined radios , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[18]  Anant Sahai,et al.  Cooperative Sensing among Cognitive Radios , 2006, 2006 IEEE International Conference on Communications.