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A Cognitive radio spectrum sensing algorithm to improve energy detection at low SNR
Subekti A.a,b, Sugihartonoa, Nana Rachmana S.a, Suksmono A.B.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, 40132, Indonesia
b Research Center for Informatics, Indonesian Institute of Sciences (LIPI), Bandung, 40135, 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]Energy detection is among the most popular spectrum sensing method for spectrum sensing due its low complexity. Unfortunately, its performance is poor at low SNR. In this paper we proposed a spectrum sensing method for cognitive radio network that improves the performance of energy detection. The proposed method based on distribution analysis using kurtosis as test statistic. This comes from the fact that distribution of received signal when a channel is occupied will be different from vacant channel. Noise tends to have a Gaussian distribution. Signal which faces multipath fading during the transmission way will have non Gaussian distribution. Sensing algorithm was tested using captured DTV signal. Result shows that our method performs well at low SNR. It achieves probability of detection of 90 % for 10 % Probability of false alarm for low SNR, below -20 dB.[/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]Cognitive radio network,Distribution analysis,DTV signals,Kurtosis,Non-gaussian distribution,Probability of detection,Probability of false alarm,Spectrum sensing[/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]Cognitive radio,DTV signal,Kurtosis,Spectrum sensing[/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/10.12928/v12i3.101[/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]