A Low Complexity Sensing Algorithm for Non-Sparse Wideband Spectrum
The vast majority of existing sub-Nyquist sampling wideband spectrum sensing (WSS) methods default to a sparse spectrum. However, research data suggests that in the near future, the wideband spectrum will no longer be sparse. This article proposes a sub-Nyquist sampling WSS algorithm that can adapt...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/16/6295 |
| _version_ | 1797431675431419904 |
|---|---|
| author | Shiyu Ren Wantong Chen Hailong Wu Dongxia Li Zhongwei Hu |
| author_facet | Shiyu Ren Wantong Chen Hailong Wu Dongxia Li Zhongwei Hu |
| author_sort | Shiyu Ren |
| collection | DOAJ |
| description | The vast majority of existing sub-Nyquist sampling wideband spectrum sensing (WSS) methods default to a sparse spectrum. However, research data suggests that in the near future, the wideband spectrum will no longer be sparse. This article proposes a sub-Nyquist sampling WSS algorithm that can adapt well to non-sparse spectrum scenarios. The algorithm continues to implement the idea of our previously proposed “no reconstruction (NoR) of spectrum” algorithm, thus having low computational complexity. The new one is actually an advanced version of the NoR algorithm, so it is called AdNoR. The key to its advancement lies in the establishment of a folded time-frequency (TF) spectrum model with the same special structure as in the fold spectrum model of the NoR algorithm. For this purpose, we have designed a comprehensive sampling technique which consists of multicoset sampling, digital fractional delay, and TF transform. It is verified by simulation that the AdNoR algorithm maintains a good sensing performance with low computational complexity in the non-sparse scenario. |
| first_indexed | 2024-03-09T09:49:42Z |
| format | Article |
| id | doaj.art-20455fa9692f4b55abeb12fc20a1f00f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:49:42Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-20455fa9692f4b55abeb12fc20a1f00f2023-12-02T00:17:51ZengMDPI AGSensors1424-82202022-08-012216629510.3390/s22166295A Low Complexity Sensing Algorithm for Non-Sparse Wideband SpectrumShiyu Ren0Wantong Chen1Hailong Wu2Dongxia Li3Zhongwei Hu4School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaSchool of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaSchool of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaSchool of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaSchool of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaThe vast majority of existing sub-Nyquist sampling wideband spectrum sensing (WSS) methods default to a sparse spectrum. However, research data suggests that in the near future, the wideband spectrum will no longer be sparse. This article proposes a sub-Nyquist sampling WSS algorithm that can adapt well to non-sparse spectrum scenarios. The algorithm continues to implement the idea of our previously proposed “no reconstruction (NoR) of spectrum” algorithm, thus having low computational complexity. The new one is actually an advanced version of the NoR algorithm, so it is called AdNoR. The key to its advancement lies in the establishment of a folded time-frequency (TF) spectrum model with the same special structure as in the fold spectrum model of the NoR algorithm. For this purpose, we have designed a comprehensive sampling technique which consists of multicoset sampling, digital fractional delay, and TF transform. It is verified by simulation that the AdNoR algorithm maintains a good sensing performance with low computational complexity in the non-sparse scenario.https://www.mdpi.com/1424-8220/22/16/6295wideband spectrum sensingnon-sparse spectrumfolded time-frequency spectrumtime-frequency subband classification |
| spellingShingle | Shiyu Ren Wantong Chen Hailong Wu Dongxia Li Zhongwei Hu A Low Complexity Sensing Algorithm for Non-Sparse Wideband Spectrum Sensors wideband spectrum sensing non-sparse spectrum folded time-frequency spectrum time-frequency subband classification |
| title | A Low Complexity Sensing Algorithm for Non-Sparse Wideband Spectrum |
| title_full | A Low Complexity Sensing Algorithm for Non-Sparse Wideband Spectrum |
| title_fullStr | A Low Complexity Sensing Algorithm for Non-Sparse Wideband Spectrum |
| title_full_unstemmed | A Low Complexity Sensing Algorithm for Non-Sparse Wideband Spectrum |
| title_short | A Low Complexity Sensing Algorithm for Non-Sparse Wideband Spectrum |
| title_sort | low complexity sensing algorithm for non sparse wideband spectrum |
| topic | wideband spectrum sensing non-sparse spectrum folded time-frequency spectrum time-frequency subband classification |
| url | https://www.mdpi.com/1424-8220/22/16/6295 |
| work_keys_str_mv | AT shiyuren alowcomplexitysensingalgorithmfornonsparsewidebandspectrum AT wantongchen alowcomplexitysensingalgorithmfornonsparsewidebandspectrum AT hailongwu alowcomplexitysensingalgorithmfornonsparsewidebandspectrum AT dongxiali alowcomplexitysensingalgorithmfornonsparsewidebandspectrum AT zhongweihu alowcomplexitysensingalgorithmfornonsparsewidebandspectrum AT shiyuren lowcomplexitysensingalgorithmfornonsparsewidebandspectrum AT wantongchen lowcomplexitysensingalgorithmfornonsparsewidebandspectrum AT hailongwu lowcomplexitysensingalgorithmfornonsparsewidebandspectrum AT dongxiali lowcomplexitysensingalgorithmfornonsparsewidebandspectrum AT zhongweihu lowcomplexitysensingalgorithmfornonsparsewidebandspectrum |