Recovery of High Order Statistics of PSK Signals Based on Low-Rank Matrix Completion
High order statistics are useful for automatic modulation recognition and parameter estimations. In this paper, we cast the problem of recovering high order statistics of PSK signals taken from nonuniform compressive samples as a one of recovering a low-rank matrix from missing or corrupted observat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10032503/ |
| _version_ | 1811165010263539712 |
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| author | Zhengli Xing Jie Zhou Zhan Ge Guanqin Huang Maohai Hu |
| author_facet | Zhengli Xing Jie Zhou Zhan Ge Guanqin Huang Maohai Hu |
| author_sort | Zhengli Xing |
| collection | DOAJ |
| description | High order statistics are useful for automatic modulation recognition and parameter estimations. In this paper, we cast the problem of recovering high order statistics of PSK signals taken from nonuniform compressive samples as a one of recovering a low-rank matrix from missing or corrupted observations. This is a new model to describe the high order statistics of PSK signals. Unlike traditional uniformly Nyquist samples, our method uses the advanced optimization technique, which is guaranteed to find the low-rank matrix by simultaneously fixing the missing entries. Simulation results demonstrate that our method achieves accurate estimates of the major portion of the high order statistics. The new technique can be used to fulfil automatic modulation recognition (AMR) and rough estimations of parameters. More specifically, low-rank structure of PSK signals is studied. In contrast to the existing <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> optimization criteria, our method proposed here is computationally more efficient and provides high accuracy. |
| first_indexed | 2024-04-10T15:30:41Z |
| format | Article |
| id | doaj.art-d7ea1ea6ee55475bb6a8878dc3806e7a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T15:30:41Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d7ea1ea6ee55475bb6a8878dc3806e7a2023-02-14T00:00:56ZengIEEEIEEE Access2169-35362023-01-0111129731298610.1109/ACCESS.2023.324124210032503Recovery of High Order Statistics of PSK Signals Based on Low-Rank Matrix CompletionZhengli Xing0https://orcid.org/0000-0002-4481-0983Jie Zhou1Zhan Ge2Guanqin Huang3Maohai Hu4Institute of Electronic Engineering, China Academy of Engineering Physics, Mianyang, ChinaInstitute of Electronic Engineering, China Academy of Engineering Physics, Mianyang, ChinaInstitute of Electronic Engineering, China Academy of Engineering Physics, Mianyang, ChinaInstitute of Electronic Engineering, China Academy of Engineering Physics, Mianyang, ChinaInstitute of Electronic Engineering, China Academy of Engineering Physics, Mianyang, ChinaHigh order statistics are useful for automatic modulation recognition and parameter estimations. In this paper, we cast the problem of recovering high order statistics of PSK signals taken from nonuniform compressive samples as a one of recovering a low-rank matrix from missing or corrupted observations. This is a new model to describe the high order statistics of PSK signals. Unlike traditional uniformly Nyquist samples, our method uses the advanced optimization technique, which is guaranteed to find the low-rank matrix by simultaneously fixing the missing entries. Simulation results demonstrate that our method achieves accurate estimates of the major portion of the high order statistics. The new technique can be used to fulfil automatic modulation recognition (AMR) and rough estimations of parameters. More specifically, low-rank structure of PSK signals is studied. In contrast to the existing <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> optimization criteria, our method proposed here is computationally more efficient and provides high accuracy.https://ieeexplore.ieee.org/document/10032503/Compressed Sensinghigh order statisticslow-rank matrix completionmodulation recognitionparameter estimationsPSK signals |
| spellingShingle | Zhengli Xing Jie Zhou Zhan Ge Guanqin Huang Maohai Hu Recovery of High Order Statistics of PSK Signals Based on Low-Rank Matrix Completion IEEE Access Compressed Sensing high order statistics low-rank matrix completion modulation recognition parameter estimations PSK signals |
| title | Recovery of High Order Statistics of PSK Signals Based on Low-Rank Matrix Completion |
| title_full | Recovery of High Order Statistics of PSK Signals Based on Low-Rank Matrix Completion |
| title_fullStr | Recovery of High Order Statistics of PSK Signals Based on Low-Rank Matrix Completion |
| title_full_unstemmed | Recovery of High Order Statistics of PSK Signals Based on Low-Rank Matrix Completion |
| title_short | Recovery of High Order Statistics of PSK Signals Based on Low-Rank Matrix Completion |
| title_sort | recovery of high order statistics of psk signals based on low rank matrix completion |
| topic | Compressed Sensing high order statistics low-rank matrix completion modulation recognition parameter estimations PSK signals |
| url | https://ieeexplore.ieee.org/document/10032503/ |
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