An Underdetermined Convolutional Blind Separation Algorithm for Time–Frequency Overlapped Wireless Communication Signals with Unknown Source Number
It has been challenging to separate the time–frequency (TF) overlapped wireless communication signals with unknown source numbers in underdetermined cases. In order to address this issue, a novel blind separation strategy based on a TF soft mask is proposed in this paper. Based on the clustering pro...
| Main Authors: | , , , , |
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/13/6534 |
| _version_ | 1797480940293849088 |
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| author | Hao Ma Xiang Zheng Lu Yu Xinrong Wu Yu Zhang |
| author_facet | Hao Ma Xiang Zheng Lu Yu Xinrong Wu Yu Zhang |
| author_sort | Hao Ma |
| collection | DOAJ |
| description | It has been challenging to separate the time–frequency (TF) overlapped wireless communication signals with unknown source numbers in underdetermined cases. In order to address this issue, a novel blind separation strategy based on a TF soft mask is proposed in this paper. Based on the clustering property of the signals in the sparse domain, the angular probability density distribution is obtained by the kernel density estimation (KDE) algorithm, and then the number of source signals is identified by detecting the peak points of the distribution. Afterward, the contribution degree function is designed according to the cosine distance to calculate the contribution degrees of the source signals in the mixed signals. The separation of the TF overlapped signals is achieved by constructing a soft mask matrix based on the contribution degrees. The simulations are performed with digital signals of the same modulation and different modulation, respectively. The results show that the proposed algorithm has better anti-aliasing and anti-noise performance than the comparison algorithms. |
| first_indexed | 2024-03-09T22:07:24Z |
| format | Article |
| id | doaj.art-6255609385584c2991661aa51a59d7bf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:07:24Z |
| publishDate | 2022-06-01 |
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| spelling | doaj.art-6255609385584c2991661aa51a59d7bf2023-11-23T19:38:20ZengMDPI AGApplied Sciences2076-34172022-06-011213653410.3390/app12136534An Underdetermined Convolutional Blind Separation Algorithm for Time–Frequency Overlapped Wireless Communication Signals with Unknown Source NumberHao Ma0Xiang Zheng1Lu Yu2Xinrong Wu3Yu Zhang4School of Communication Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaSchool of Communication Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaSchool of Communication Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaSchool of Communication Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaSchool of Communication Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaIt has been challenging to separate the time–frequency (TF) overlapped wireless communication signals with unknown source numbers in underdetermined cases. In order to address this issue, a novel blind separation strategy based on a TF soft mask is proposed in this paper. Based on the clustering property of the signals in the sparse domain, the angular probability density distribution is obtained by the kernel density estimation (KDE) algorithm, and then the number of source signals is identified by detecting the peak points of the distribution. Afterward, the contribution degree function is designed according to the cosine distance to calculate the contribution degrees of the source signals in the mixed signals. The separation of the TF overlapped signals is achieved by constructing a soft mask matrix based on the contribution degrees. The simulations are performed with digital signals of the same modulation and different modulation, respectively. The results show that the proposed algorithm has better anti-aliasing and anti-noise performance than the comparison algorithms.https://www.mdpi.com/2076-3417/12/13/6534underdetermined blind separationTF overlapped signalsTF soft maskKDE algorithmcontribution degree function |
| spellingShingle | Hao Ma Xiang Zheng Lu Yu Xinrong Wu Yu Zhang An Underdetermined Convolutional Blind Separation Algorithm for Time–Frequency Overlapped Wireless Communication Signals with Unknown Source Number Applied Sciences underdetermined blind separation TF overlapped signals TF soft mask KDE algorithm contribution degree function |
| title | An Underdetermined Convolutional Blind Separation Algorithm for Time–Frequency Overlapped Wireless Communication Signals with Unknown Source Number |
| title_full | An Underdetermined Convolutional Blind Separation Algorithm for Time–Frequency Overlapped Wireless Communication Signals with Unknown Source Number |
| title_fullStr | An Underdetermined Convolutional Blind Separation Algorithm for Time–Frequency Overlapped Wireless Communication Signals with Unknown Source Number |
| title_full_unstemmed | An Underdetermined Convolutional Blind Separation Algorithm for Time–Frequency Overlapped Wireless Communication Signals with Unknown Source Number |
| title_short | An Underdetermined Convolutional Blind Separation Algorithm for Time–Frequency Overlapped Wireless Communication Signals with Unknown Source Number |
| title_sort | underdetermined convolutional blind separation algorithm for time frequency overlapped wireless communication signals with unknown source number |
| topic | underdetermined blind separation TF overlapped signals TF soft mask KDE algorithm contribution degree function |
| url | https://www.mdpi.com/2076-3417/12/13/6534 |
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