DOUBLE THRESHOLD SPECTRUM SENSING WITH OPTIMIZATION OF SAMPLES IN COGNITIVE RADIO NETWORKS

Chilakala Sudhamani

doi:10.31436/iiumej.v22i2.1597

Authors

Chilakala Sudhamani CMR Technical Campus https://orcid.org/0000-0002-8823-9053

DOI:

https://doi.org/10.31436/iiumej.v22i2.1597

Keywords:

Cognitive Radio, Spectrum Sensing, Detection THreshold, Optimization

Abstract

In cognitive radio networks spectrum sensing plays a vital role to identify the presence or absence of the primary user. In conventional spectrum sensing one secondary user will make a final decision regarding the availability of licensed spectrum. But Secondary user fail to make a correct detection about the presence of the primary user if he is in fading environment and it causes interference to the licensed users. Therefore to avoid interference to the licensed users and to make correct detection, number of samples is increased, Which increases the probability of detection. In this paper the optimization of samples is proposed to reduce the system overhead and also to increase the propagation time. Simulation results show the optimized value of samples for a given probability of false alarm and also the variation of probability of detection with optimized samples and false alarm is shown in the results.

ABSTRAK: Dalam rangkaian radio kognitif, penginderaan spektrum memainkan peranan penting untuk mengenal pasti kehadiran atau ketiadaan pengguna utama. Dalam penginderaan spektrum konvensional, seorang pengguna sekunder akan membuat keputusan akhir mengenai ketersediaan spektrum berlesen. Tetapi pengguna Sekunder gagal membuat pengesanan yang betul mengenai kehadiran pengguna utama jika dia berada dalam persekitaran yang pudar dan menyebabkan gangguan kepada pengguna yang berlesen. Oleh itu untuk mengelakkan gangguan kepada pengguna berlesen dan membuat pengesanan yang betul, jumlah sampel meningkat, yang meningkatkan kemungkinan pengesanan. Dalam makalah ini pengoptimuman sampel dicadangkan untuk mengurangi overhead sistem dan juga untuk meningkatkan waktu penyebaran. Hasil simulasi menunjukkan nilai sampel yang dioptimumkan untuk kebarangkalian penggera palsu dan juga variasi kebarangkalian pengesanan dengan sampel yang dioptimumkan dan penggera palsu ditunjukkan dalam hasil.

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References

Federal Communications Commission. (2002) Spectrum policy task force. ET Docket No. 02-35.

Sharma V, Joshi S. (2018) A literature review on spectrum sensing in cognitive radio applications. Second International Conference on Intelligent Computing and Control Systems: July 2018. DOI: https://doi.org/10.1109/ICCONS.2018.8663089

Mitola J, Maguire MG. (1999) Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6:13-18. DOI: https://doi.org/10.1109/98.788210

Cabric D, Mishra SM, Brodersen RW. (2004) Implementation issues in spectrum sensing for cognitive radios. Proceedings of Asilomar Conference on Signals, Systems, and Computers, 1:772-776. DOI: https://doi.org/10.1109/ACSSC.2004.1399240

Yucek T, Arslan D. (2009) A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys and Tutorials, 11: 116-130. DOI: https://doi.org/10.1109/SURV.2009.090109

Ali SS, Liu C, Jin M. (2014) Minimum eigenvalue detection for spectrum sensing. International Journal of Electrical and Computer Engineering (IJECE), 4: 623-630. DOI: https://doi.org/10.11591/ijece.v4i4.6226

Liu SQ, Hu BJ, Wang XY. (2012) Hierarchical cooperative spectrum sensing based on double thresholds energy detection. IEEE Communications Letters, 16: 1096-1099. DOI: https://doi.org/10.1109/LCOMM.2012.050112.120765

Xie J, Chen J. (2012) An adaptive double threshold spectrum sensing algorithm under noise uncertainty. Computer and Information Technology, 1: 824-827. DOI: https://doi.org/10.1109/CIT.2012.171

Ribas ADOP, Dias US. (2015) On the double threshold energy detection-based spectrum sensing over fading channel. Radio and Wireless Symposium: 2015, San Diego, CA.

Steve Arul U, Salai Chandira Rajan S. (2016) Spectrum management techniques using cognitive radios. Cognitive Radio Technology: International Journal of Data Mining Techniques and Applications, 5: 79-82. DOI: https://doi.org/10.20894/IJDMTA.102.005.001.019

Ganesan G, Li YG. (2007) Cooperative spectrum sensing in cognitive radio–part I: two user DOI: https://doi.org/10.1109/PIMRC.2007.4394020

networks. IEEE Transaction on Wireless Communications, 6: 2204–2213

Christian I, Sangman M, Chung I, Lee J. (2012) Spectrum mobility in cognitive radio networks. IEEE Communications Magazine, 50(6): 114-121. DOI: https://doi.org/10.1109/MCOM.2012.6211495

Xuping Z, Jianguo P. (2007) Energy-detection based spectrum sensing for cognitive radio. IET Conference on Wireless, Mobile and Sensor Networks: 944-947. DOI: https://doi.org/10.1049/cp:20070306

Lee SH, Oh DC, Lee YH. (2009) Hard decision combining based cooperative spectrum sensing in cognitive radio systems. International Conference on Wireless Communications and Mobile Computing: 2009, Leipzig, Germany. 906-910. DOI: https://doi.org/10.1145/1582379.1582576

Hou F, Chen X, Huang H, Jing X. (2016) Throughput performance improvement in cognitive radio networks based on spectru prediction. 16th International Symposium on Communications and Information Technologies: Qingdao, China. DOI: https://doi.org/10.1109/ISCIT.2016.7751715

Wu J, Luo T, Li J, Yue G. (2009) A cooperative double threshold energy detection algorithm in cognitive radio systems. IEEE International Conference on Wireless Communication, Networking & Mobile Computing., 1-4. doi 10.1109/WICOM.2009.5303377 DOI: https://doi.org/10.1109/WICOM.2009.5303377

Owayed A, Mohammed ZA, Mosa AA. (2010) Probabilities of detection and false alarm in multitaper based spectrum sensing for cognitive radio systems in AWGN. IEEE International Conference on Communication Systems: 2010 Singapore. 579-584. DOI: https://doi.org/10.1109/ICCS.2010.5686717

Verma P, Singh B. (2015) Simulation study of double threshold energy detection method for cognitive radios. 2nd International Conference on Signal Processing and Integrated Networks: Noida. 232- 236. DOI: https://doi.org/10.1109/SPIN.2015.7095276

Sengupta A, Chattopadhyay S, Ghatak SR, Biswas V. (2019) Two-Stage spectrum sensing model for varying SNR conditions in cognitive radio network. International Conference on Electrical, Electronics and Computer Engineering: ALIGARH, India. DOI: https://doi.org/10.1109/UPCON47278.2019.8980250