A Novel Matrix Optimization for Compressive Sampling-Based Sub-Nyquist OFDM Receiver in Cognitive Radio
Abstract Modulated wideband converter is the most commonly accepted technique for implementing sub-Nyquist compressive sampling-based wireless receiver to reduce the analog and digital processing complexity when detecting wideband spectrum for cognitive radio systems. However, the issue of non-optim...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Hao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Circuits, systems and signal processing - Boston, Mass. : Birkhäuser, 1982, 37(2018), 11 vom: 26. März, Seite 5069-5086 |
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| Übergeordnetes Werk: |
volume:37 ; year:2018 ; number:11 ; day:26 ; month:03 ; pages:5069-5086 |
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| DOI / URN: |
10.1007/s00034-018-0806-8 |
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SPR000566837 |
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| 520 | |a Abstract Modulated wideband converter is the most commonly accepted technique for implementing sub-Nyquist compressive sampling-based wireless receiver to reduce the analog and digital processing complexity when detecting wideband spectrum for cognitive radio systems. However, the issue of non-optimal mutual coherence, which leads to a higher receiving bit error rate, has not been considered in existing compressive sampling-based cognitive radio studies. Furthermore, existing theoretical compressive sampling-based solutions cannot be directly applied because typical modulated wideband converter-based designs use fixed parameters that cannot be easily updated during their sampling operations. This paper presents a novel matrix optimization which can be incorporated into modulated wideband converter-based cognitive radio to enhance its detection accuracy for OFDM signals. The proposed approach can also be predetermined to reduce the computation complexity, while remains compatible with standard digital OFDM receiver’s operation. Simulation results show that our proposed system can consistently produce smaller compressive sampling reconstruction error in terms of lower bit error rate under various operating conditions compared to existing systems. | ||
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| 650 | 4 | |a Compressive sampling |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Vun, Chan Hua |4 aut | |
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10.1007/s00034-018-0806-8 doi (DE-627)SPR000566837 (SPR)s00034-018-0806-8-e DE-627 ger DE-627 rakwb eng Chen, Hao verfasserin (orcid)0000-0003-2322-494X aut A Novel Matrix Optimization for Compressive Sampling-Based Sub-Nyquist OFDM Receiver in Cognitive Radio 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Modulated wideband converter is the most commonly accepted technique for implementing sub-Nyquist compressive sampling-based wireless receiver to reduce the analog and digital processing complexity when detecting wideband spectrum for cognitive radio systems. However, the issue of non-optimal mutual coherence, which leads to a higher receiving bit error rate, has not been considered in existing compressive sampling-based cognitive radio studies. Furthermore, existing theoretical compressive sampling-based solutions cannot be directly applied because typical modulated wideband converter-based designs use fixed parameters that cannot be easily updated during their sampling operations. This paper presents a novel matrix optimization which can be incorporated into modulated wideband converter-based cognitive radio to enhance its detection accuracy for OFDM signals. The proposed approach can also be predetermined to reduce the computation complexity, while remains compatible with standard digital OFDM receiver’s operation. Simulation results show that our proposed system can consistently produce smaller compressive sampling reconstruction error in terms of lower bit error rate under various operating conditions compared to existing systems. Cognitive radio (dpeaa)DE-He213 Sub-Nyquist OFDM receiver (dpeaa)DE-He213 Compressive sampling (dpeaa)DE-He213 Mutual coherence optimization (dpeaa)DE-He213 Vun, Chan Hua aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 37(2018), 11 vom: 26. März, Seite 5069-5086 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:37 year:2018 number:11 day:26 month:03 pages:5069-5086 https://dx.doi.org/10.1007/s00034-018-0806-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 11 26 03 5069-5086 |
| spelling |
10.1007/s00034-018-0806-8 doi (DE-627)SPR000566837 (SPR)s00034-018-0806-8-e DE-627 ger DE-627 rakwb eng Chen, Hao verfasserin (orcid)0000-0003-2322-494X aut A Novel Matrix Optimization for Compressive Sampling-Based Sub-Nyquist OFDM Receiver in Cognitive Radio 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Modulated wideband converter is the most commonly accepted technique for implementing sub-Nyquist compressive sampling-based wireless receiver to reduce the analog and digital processing complexity when detecting wideband spectrum for cognitive radio systems. However, the issue of non-optimal mutual coherence, which leads to a higher receiving bit error rate, has not been considered in existing compressive sampling-based cognitive radio studies. Furthermore, existing theoretical compressive sampling-based solutions cannot be directly applied because typical modulated wideband converter-based designs use fixed parameters that cannot be easily updated during their sampling operations. This paper presents a novel matrix optimization which can be incorporated into modulated wideband converter-based cognitive radio to enhance its detection accuracy for OFDM signals. The proposed approach can also be predetermined to reduce the computation complexity, while remains compatible with standard digital OFDM receiver’s operation. Simulation results show that our proposed system can consistently produce smaller compressive sampling reconstruction error in terms of lower bit error rate under various operating conditions compared to existing systems. Cognitive radio (dpeaa)DE-He213 Sub-Nyquist OFDM receiver (dpeaa)DE-He213 Compressive sampling (dpeaa)DE-He213 Mutual coherence optimization (dpeaa)DE-He213 Vun, Chan Hua aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 37(2018), 11 vom: 26. März, Seite 5069-5086 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:37 year:2018 number:11 day:26 month:03 pages:5069-5086 https://dx.doi.org/10.1007/s00034-018-0806-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 11 26 03 5069-5086 |
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10.1007/s00034-018-0806-8 doi (DE-627)SPR000566837 (SPR)s00034-018-0806-8-e DE-627 ger DE-627 rakwb eng Chen, Hao verfasserin (orcid)0000-0003-2322-494X aut A Novel Matrix Optimization for Compressive Sampling-Based Sub-Nyquist OFDM Receiver in Cognitive Radio 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Modulated wideband converter is the most commonly accepted technique for implementing sub-Nyquist compressive sampling-based wireless receiver to reduce the analog and digital processing complexity when detecting wideband spectrum for cognitive radio systems. However, the issue of non-optimal mutual coherence, which leads to a higher receiving bit error rate, has not been considered in existing compressive sampling-based cognitive radio studies. Furthermore, existing theoretical compressive sampling-based solutions cannot be directly applied because typical modulated wideband converter-based designs use fixed parameters that cannot be easily updated during their sampling operations. This paper presents a novel matrix optimization which can be incorporated into modulated wideband converter-based cognitive radio to enhance its detection accuracy for OFDM signals. The proposed approach can also be predetermined to reduce the computation complexity, while remains compatible with standard digital OFDM receiver’s operation. Simulation results show that our proposed system can consistently produce smaller compressive sampling reconstruction error in terms of lower bit error rate under various operating conditions compared to existing systems. Cognitive radio (dpeaa)DE-He213 Sub-Nyquist OFDM receiver (dpeaa)DE-He213 Compressive sampling (dpeaa)DE-He213 Mutual coherence optimization (dpeaa)DE-He213 Vun, Chan Hua aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 37(2018), 11 vom: 26. März, Seite 5069-5086 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:37 year:2018 number:11 day:26 month:03 pages:5069-5086 https://dx.doi.org/10.1007/s00034-018-0806-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 11 26 03 5069-5086 |
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10.1007/s00034-018-0806-8 doi (DE-627)SPR000566837 (SPR)s00034-018-0806-8-e DE-627 ger DE-627 rakwb eng Chen, Hao verfasserin (orcid)0000-0003-2322-494X aut A Novel Matrix Optimization for Compressive Sampling-Based Sub-Nyquist OFDM Receiver in Cognitive Radio 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Modulated wideband converter is the most commonly accepted technique for implementing sub-Nyquist compressive sampling-based wireless receiver to reduce the analog and digital processing complexity when detecting wideband spectrum for cognitive radio systems. However, the issue of non-optimal mutual coherence, which leads to a higher receiving bit error rate, has not been considered in existing compressive sampling-based cognitive radio studies. Furthermore, existing theoretical compressive sampling-based solutions cannot be directly applied because typical modulated wideband converter-based designs use fixed parameters that cannot be easily updated during their sampling operations. This paper presents a novel matrix optimization which can be incorporated into modulated wideband converter-based cognitive radio to enhance its detection accuracy for OFDM signals. The proposed approach can also be predetermined to reduce the computation complexity, while remains compatible with standard digital OFDM receiver’s operation. Simulation results show that our proposed system can consistently produce smaller compressive sampling reconstruction error in terms of lower bit error rate under various operating conditions compared to existing systems. Cognitive radio (dpeaa)DE-He213 Sub-Nyquist OFDM receiver (dpeaa)DE-He213 Compressive sampling (dpeaa)DE-He213 Mutual coherence optimization (dpeaa)DE-He213 Vun, Chan Hua aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 37(2018), 11 vom: 26. März, Seite 5069-5086 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:37 year:2018 number:11 day:26 month:03 pages:5069-5086 https://dx.doi.org/10.1007/s00034-018-0806-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 11 26 03 5069-5086 |
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10.1007/s00034-018-0806-8 doi (DE-627)SPR000566837 (SPR)s00034-018-0806-8-e DE-627 ger DE-627 rakwb eng Chen, Hao verfasserin (orcid)0000-0003-2322-494X aut A Novel Matrix Optimization for Compressive Sampling-Based Sub-Nyquist OFDM Receiver in Cognitive Radio 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Modulated wideband converter is the most commonly accepted technique for implementing sub-Nyquist compressive sampling-based wireless receiver to reduce the analog and digital processing complexity when detecting wideband spectrum for cognitive radio systems. However, the issue of non-optimal mutual coherence, which leads to a higher receiving bit error rate, has not been considered in existing compressive sampling-based cognitive radio studies. Furthermore, existing theoretical compressive sampling-based solutions cannot be directly applied because typical modulated wideband converter-based designs use fixed parameters that cannot be easily updated during their sampling operations. This paper presents a novel matrix optimization which can be incorporated into modulated wideband converter-based cognitive radio to enhance its detection accuracy for OFDM signals. The proposed approach can also be predetermined to reduce the computation complexity, while remains compatible with standard digital OFDM receiver’s operation. Simulation results show that our proposed system can consistently produce smaller compressive sampling reconstruction error in terms of lower bit error rate under various operating conditions compared to existing systems. Cognitive radio (dpeaa)DE-He213 Sub-Nyquist OFDM receiver (dpeaa)DE-He213 Compressive sampling (dpeaa)DE-He213 Mutual coherence optimization (dpeaa)DE-He213 Vun, Chan Hua aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 37(2018), 11 vom: 26. März, Seite 5069-5086 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:37 year:2018 number:11 day:26 month:03 pages:5069-5086 https://dx.doi.org/10.1007/s00034-018-0806-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 11 26 03 5069-5086 |
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Chen, Hao |
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Chen, Hao misc Cognitive radio misc Sub-Nyquist OFDM receiver misc Compressive sampling misc Mutual coherence optimization A Novel Matrix Optimization for Compressive Sampling-Based Sub-Nyquist OFDM Receiver in Cognitive Radio |
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A Novel Matrix Optimization for Compressive Sampling-Based Sub-Nyquist OFDM Receiver in Cognitive Radio Cognitive radio (dpeaa)DE-He213 Sub-Nyquist OFDM receiver (dpeaa)DE-He213 Compressive sampling (dpeaa)DE-He213 Mutual coherence optimization (dpeaa)DE-He213 |
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novel matrix optimization for compressive sampling-based sub-nyquist ofdm receiver in cognitive radio |
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A Novel Matrix Optimization for Compressive Sampling-Based Sub-Nyquist OFDM Receiver in Cognitive Radio |
| abstract |
Abstract Modulated wideband converter is the most commonly accepted technique for implementing sub-Nyquist compressive sampling-based wireless receiver to reduce the analog and digital processing complexity when detecting wideband spectrum for cognitive radio systems. However, the issue of non-optimal mutual coherence, which leads to a higher receiving bit error rate, has not been considered in existing compressive sampling-based cognitive radio studies. Furthermore, existing theoretical compressive sampling-based solutions cannot be directly applied because typical modulated wideband converter-based designs use fixed parameters that cannot be easily updated during their sampling operations. This paper presents a novel matrix optimization which can be incorporated into modulated wideband converter-based cognitive radio to enhance its detection accuracy for OFDM signals. The proposed approach can also be predetermined to reduce the computation complexity, while remains compatible with standard digital OFDM receiver’s operation. Simulation results show that our proposed system can consistently produce smaller compressive sampling reconstruction error in terms of lower bit error rate under various operating conditions compared to existing systems. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstractGer |
Abstract Modulated wideband converter is the most commonly accepted technique for implementing sub-Nyquist compressive sampling-based wireless receiver to reduce the analog and digital processing complexity when detecting wideband spectrum for cognitive radio systems. However, the issue of non-optimal mutual coherence, which leads to a higher receiving bit error rate, has not been considered in existing compressive sampling-based cognitive radio studies. Furthermore, existing theoretical compressive sampling-based solutions cannot be directly applied because typical modulated wideband converter-based designs use fixed parameters that cannot be easily updated during their sampling operations. This paper presents a novel matrix optimization which can be incorporated into modulated wideband converter-based cognitive radio to enhance its detection accuracy for OFDM signals. The proposed approach can also be predetermined to reduce the computation complexity, while remains compatible with standard digital OFDM receiver’s operation. Simulation results show that our proposed system can consistently produce smaller compressive sampling reconstruction error in terms of lower bit error rate under various operating conditions compared to existing systems. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Modulated wideband converter is the most commonly accepted technique for implementing sub-Nyquist compressive sampling-based wireless receiver to reduce the analog and digital processing complexity when detecting wideband spectrum for cognitive radio systems. However, the issue of non-optimal mutual coherence, which leads to a higher receiving bit error rate, has not been considered in existing compressive sampling-based cognitive radio studies. Furthermore, existing theoretical compressive sampling-based solutions cannot be directly applied because typical modulated wideband converter-based designs use fixed parameters that cannot be easily updated during their sampling operations. This paper presents a novel matrix optimization which can be incorporated into modulated wideband converter-based cognitive radio to enhance its detection accuracy for OFDM signals. The proposed approach can also be predetermined to reduce the computation complexity, while remains compatible with standard digital OFDM receiver’s operation. Simulation results show that our proposed system can consistently produce smaller compressive sampling reconstruction error in terms of lower bit error rate under various operating conditions compared to existing systems. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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A Novel Matrix Optimization for Compressive Sampling-Based Sub-Nyquist OFDM Receiver in Cognitive Radio |
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https://dx.doi.org/10.1007/s00034-018-0806-8 |
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Vun, Chan Hua |
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| doi_str |
10.1007/s00034-018-0806-8 |
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2024-07-03T16:57:44.185Z |
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