Low Complexity Cyclic Feature Recovery Based on Compressed Sampling
To extract statistic features of communication signal from compressive samples, such as cyclostationary property, full-scale signal reconstruction is not actually necessary or somehow expensive. However, direct reconstruction of cyclic feature may not be practical due to the relative high processing...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/946457 |
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| _version_ | 1832545249835089920 |
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| author | Zhuo Sun Jia Hou Siyuan Liu Sese Wang Xuantong Chen |
| author_facet | Zhuo Sun Jia Hou Siyuan Liu Sese Wang Xuantong Chen |
| author_sort | Zhuo Sun |
| collection | DOAJ |
| description | To extract statistic features of communication signal from compressive samples, such as cyclostationary property, full-scale signal reconstruction is not actually necessary or somehow expensive. However, direct reconstruction of cyclic feature may not be practical due to the relative high processing complexity. In this paper, we propose a new cyclic feature recovery approach based on the reconstruction of autocorrelation sequence from sub-Nyquist samples, which can reduce the computation complexity and memory consumption significantly, while the recovery performance remains well in the same compressive ratio. Through theoretical analyses and simulations, we conducted to show and verify our statements and conclusions. |
| format | Article |
| id | doaj-art-99d65cd5b70f49ee8c98bdaff8f36660 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-99d65cd5b70f49ee8c98bdaff8f366602025-02-03T07:26:21ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-10-011110.1155/2015/946457946457Low Complexity Cyclic Feature Recovery Based on Compressed SamplingZhuo Sun0Jia Hou1Siyuan Liu2Sese Wang3Xuantong Chen4 School of electronics & Information, Soochow University, Suzhou 215006, China School of electronics & Information, Soochow University, Suzhou 215006, China School of electronics & Information, Soochow University, Suzhou 215006, China School of electronics & Information, Soochow University, Suzhou 215006, China School of electronics & Information, Soochow University, Suzhou 215006, ChinaTo extract statistic features of communication signal from compressive samples, such as cyclostationary property, full-scale signal reconstruction is not actually necessary or somehow expensive. However, direct reconstruction of cyclic feature may not be practical due to the relative high processing complexity. In this paper, we propose a new cyclic feature recovery approach based on the reconstruction of autocorrelation sequence from sub-Nyquist samples, which can reduce the computation complexity and memory consumption significantly, while the recovery performance remains well in the same compressive ratio. Through theoretical analyses and simulations, we conducted to show and verify our statements and conclusions.https://doi.org/10.1155/2015/946457 |
| spellingShingle | Zhuo Sun Jia Hou Siyuan Liu Sese Wang Xuantong Chen Low Complexity Cyclic Feature Recovery Based on Compressed Sampling International Journal of Distributed Sensor Networks |
| title | Low Complexity Cyclic Feature Recovery Based on Compressed Sampling |
| title_full | Low Complexity Cyclic Feature Recovery Based on Compressed Sampling |
| title_fullStr | Low Complexity Cyclic Feature Recovery Based on Compressed Sampling |
| title_full_unstemmed | Low Complexity Cyclic Feature Recovery Based on Compressed Sampling |
| title_short | Low Complexity Cyclic Feature Recovery Based on Compressed Sampling |
| title_sort | low complexity cyclic feature recovery based on compressed sampling |
| url | https://doi.org/10.1155/2015/946457 |
| work_keys_str_mv | AT zhuosun lowcomplexitycyclicfeaturerecoverybasedoncompressedsampling AT jiahou lowcomplexitycyclicfeaturerecoverybasedoncompressedsampling AT siyuanliu lowcomplexitycyclicfeaturerecoverybasedoncompressedsampling AT sesewang lowcomplexitycyclicfeaturerecoverybasedoncompressedsampling AT xuantongchen lowcomplexitycyclicfeaturerecoverybasedoncompressedsampling |