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A blind spectrum sensing for cognitive radio based on Jarque-Bera normality test
Subekti A.a, Nana Rachmana S.b, Sugihartonob, Suksmono A.B.b
a Research Center for Informatics, Indonesian Institute of Sciences (LIPI), Bandung, 40135, Indonesia
b School of Electrical Engineering and Informatics, Bandung Institute of Technology (ITB), Bandung, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2016, School of Electrical Engineering and Informatics. All rights reserved.A cognitive radio is only able to set up communication using unused frequencies or vacant channels. Spectrum sensing is mandatory task for cognitive radio to detect the vacant channels. The detection faces some challenges due to the required performance and limited knowledge on the primary signals. A blind spectrum sensing which can work without the knowledge of the primary signal is preferable. In this paper we propose a blind spectrum sensing method for cognitive radio network. The proposed method based on the difference on distribution of the condition between when the transmission from primary user is active and when it’s inactive. If it is inactive, the received signal will contain only noise which is Gaussian. It is different from the distribution of signal which is contaminated with noise. A normality test using the Jarque-Bera test statistic is used for the detection of the event. The target of the analysis is both the real and the imaginary parts of FFT’s output. The proposed algorithm was tested to detect the DTV signal. Results show that our method is performed better than previous similar method.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Blind spectrum sensing,Cognitive radio,Jarque-Bera (JB) test,Kurtosis,Probability of false alarm,Probability of miss detection,Skewness[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.15676/ijeei.2016.8.2.12[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]