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Kurtosis based spectrum sensing for cognitive wireless cloud computing network
Subekti A.a, Sugihartonob, Suksmono A.B.b
a Research Center for Informatics, Indonesian Institute of Sciences, Indonesia
b School of Electrical Engineering and Informatics, Institut Teknologi Bandung, 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]Spectrum sensing method for cognitive wireless cloud computing (CWC) network is very challenging since there are several different communication systems should be detected at very low SNR (as low as 22 dB). In this paper, we propose a kurtosis based spectrum sensing method which can be applied efficiently in such environment. The proposed method uses kurtosis estimation of received samples. Its value will be equal or close to 3 when only gaussian noise samples exist in the received signal. This kurtosis estimation’s used to distinguish between the present or absent of primary signal by comparing with a predefined threshold. Simulation’s done to evaluate its performance. Results show that the proposed method performs much better than energy detection especially at low SNR, even below 20 dB. It also gives benefit in much simple implementation for CWC network since it doesn’t need knowledge of primary signal’s parameters. © 2012 IEEE.[/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]Energy detection,kurtosis,Low SNR,Probability of detection,Probability of false alarm,Received signals,Spectrum sensing,Very low SNR[/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 wireless cloud,kurtosis,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=”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/10.1109/ICCCSN.2012.6215720[/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]