Ultrawideband Channel Estimation: A Bayesian Compressive Sensing Strategy Based on Statistical Sparsity
To cope with the formidable sampling rate required by Nyquist criterion, compressive sensing (CS) has been recently adopted for ultrawideband (UWB) channel estimation. In this paper, exploiting the statistical sparsity of real UWB signals in the basis formed by eigenvectors, we develop a new CS dict...
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| Autor*in: |
Xiantao Cheng [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
ultrawideband channel estimation random projection measurements eigenvalues and eigenfunctions |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on vehicular technology - New York, NY : IEEE, 1967, 64(2015), 5, Seite 1819-1832 |
|---|---|
| Übergeordnetes Werk: |
volume:64 ; year:2015 ; number:5 ; pages:1819-1832 |
| Links: |
|---|
| DOI / URN: |
10.1109/TVT.2014.2340894 |
|---|
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OLC1964446732 |
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| 520 | |a To cope with the formidable sampling rate required by Nyquist criterion, compressive sensing (CS) has been recently adopted for ultrawideband (UWB) channel estimation. In this paper, exploiting the statistical sparsity of real UWB signals in the basis formed by eigenvectors, we develop a new CS dictionary called eigendictionary, which enables the use of CS for UWB channel estimation. With respect to the eigendictionary, the expansion vector of UWB signals is sparse and exhibits an additional structure in the form of statistically significant coefficients occurring in clusters. Capitalizing on this structure, we propose two novel Bayesian CS (BCS) algorithms to efficiently reconstruct UWB signals from a small collection of random projection measurements. Furthermore, by utilizing the common sparsity profile inherent in UWB signals, we extend the proposed Bayesian algorithms to multitask (MT) versions, which can simultaneously recover multiple UWB signals if available. Since the statistical connection between different UWB signals is exploited, the developed MT-BCS can obtain better performance than the single-task version. Extensive simulations using real UWB data show that the proposed schemes considerably reduce the requirement on sampling rate and present excellent performance compared with the traditional correlator and other CS-based channel estimation schemes. | ||
| 650 | 4 | |a signal reconstruction | |
| 650 | 4 | |a eigenvectors | |
| 650 | 4 | |a ultra wideband communication | |
| 650 | 4 | |a eigendictionary | |
| 650 | 4 | |a ultrawideband channel estimation | |
| 650 | 4 | |a Vectors | |
| 650 | 4 | |a Bayesian compressive sensing | |
| 650 | 4 | |a statistical sparsity | |
| 650 | 4 | |a expansion vector | |
| 650 | 4 | |a multiple UWB signals | |
| 650 | 4 | |a compressed sensing | |
| 650 | 4 | |a Bayes methods | |
| 650 | 4 | |a Ultra wideband technology | |
| 650 | 4 | |a Dictionaries | |
| 650 | 4 | |a random projection measurements | |
| 650 | 4 | |a eigenvalues and eigenfunctions | |
| 650 | 4 | |a common sparsity profile | |
| 650 | 4 | |a Bayesian algorithms | |
| 650 | 4 | |a channel estimation | |
| 650 | 4 | |a Nyquist criterion | |
| 650 | 4 | |a Receivers | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Sparsity | |
| 650 | 4 | |a Eigenvectors | |
| 650 | 4 | |a Bayesian statistical decision theory | |
| 650 | 4 | |a Technology application | |
| 650 | 4 | |a Channeling (Physics) | |
| 650 | 4 | |a Research | |
| 650 | 4 | |a Usage | |
| 700 | 0 | |a Mengyao Wang |4 oth | |
| 700 | 0 | |a Yong Liang Guan |4 oth | |
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10.1109/TVT.2014.2340894 doi PQ20160617 (DE-627)OLC1964446732 (DE-599)GBVOLC1964446732 (PRQ)c2334-52513c57c32ca93dc91d5526b9326ff2c957638427fa70ee301502a2f6e8f8d20 (KEY)0030991520150000064000501819ultrawidebandchannelestimationabayesiancompressive DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Xiantao Cheng verfasserin aut Ultrawideband Channel Estimation: A Bayesian Compressive Sensing Strategy Based on Statistical Sparsity 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier To cope with the formidable sampling rate required by Nyquist criterion, compressive sensing (CS) has been recently adopted for ultrawideband (UWB) channel estimation. In this paper, exploiting the statistical sparsity of real UWB signals in the basis formed by eigenvectors, we develop a new CS dictionary called eigendictionary, which enables the use of CS for UWB channel estimation. With respect to the eigendictionary, the expansion vector of UWB signals is sparse and exhibits an additional structure in the form of statistically significant coefficients occurring in clusters. Capitalizing on this structure, we propose two novel Bayesian CS (BCS) algorithms to efficiently reconstruct UWB signals from a small collection of random projection measurements. Furthermore, by utilizing the common sparsity profile inherent in UWB signals, we extend the proposed Bayesian algorithms to multitask (MT) versions, which can simultaneously recover multiple UWB signals if available. Since the statistical connection between different UWB signals is exploited, the developed MT-BCS can obtain better performance than the single-task version. Extensive simulations using real UWB data show that the proposed schemes considerably reduce the requirement on sampling rate and present excellent performance compared with the traditional correlator and other CS-based channel estimation schemes. signal reconstruction eigenvectors ultra wideband communication eigendictionary ultrawideband channel estimation Vectors Bayesian compressive sensing statistical sparsity expansion vector multiple UWB signals compressed sensing Bayes methods Ultra wideband technology Dictionaries random projection measurements eigenvalues and eigenfunctions common sparsity profile Bayesian algorithms channel estimation Nyquist criterion Receivers Algorithms Sparsity Eigenvectors Bayesian statistical decision theory Technology application Channeling (Physics) Research Usage Mengyao Wang oth Yong Liang Guan oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 64(2015), 5, Seite 1819-1832 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:64 year:2015 number:5 pages:1819-1832 http://dx.doi.org/10.1109/TVT.2014.2340894 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6860314 http://search.proquest.com/docview/1699221922 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 64 2015 5 1819-1832 |
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10.1109/TVT.2014.2340894 doi PQ20160617 (DE-627)OLC1964446732 (DE-599)GBVOLC1964446732 (PRQ)c2334-52513c57c32ca93dc91d5526b9326ff2c957638427fa70ee301502a2f6e8f8d20 (KEY)0030991520150000064000501819ultrawidebandchannelestimationabayesiancompressive DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Xiantao Cheng verfasserin aut Ultrawideband Channel Estimation: A Bayesian Compressive Sensing Strategy Based on Statistical Sparsity 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier To cope with the formidable sampling rate required by Nyquist criterion, compressive sensing (CS) has been recently adopted for ultrawideband (UWB) channel estimation. In this paper, exploiting the statistical sparsity of real UWB signals in the basis formed by eigenvectors, we develop a new CS dictionary called eigendictionary, which enables the use of CS for UWB channel estimation. With respect to the eigendictionary, the expansion vector of UWB signals is sparse and exhibits an additional structure in the form of statistically significant coefficients occurring in clusters. Capitalizing on this structure, we propose two novel Bayesian CS (BCS) algorithms to efficiently reconstruct UWB signals from a small collection of random projection measurements. Furthermore, by utilizing the common sparsity profile inherent in UWB signals, we extend the proposed Bayesian algorithms to multitask (MT) versions, which can simultaneously recover multiple UWB signals if available. Since the statistical connection between different UWB signals is exploited, the developed MT-BCS can obtain better performance than the single-task version. Extensive simulations using real UWB data show that the proposed schemes considerably reduce the requirement on sampling rate and present excellent performance compared with the traditional correlator and other CS-based channel estimation schemes. signal reconstruction eigenvectors ultra wideband communication eigendictionary ultrawideband channel estimation Vectors Bayesian compressive sensing statistical sparsity expansion vector multiple UWB signals compressed sensing Bayes methods Ultra wideband technology Dictionaries random projection measurements eigenvalues and eigenfunctions common sparsity profile Bayesian algorithms channel estimation Nyquist criterion Receivers Algorithms Sparsity Eigenvectors Bayesian statistical decision theory Technology application Channeling (Physics) Research Usage Mengyao Wang oth Yong Liang Guan oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 64(2015), 5, Seite 1819-1832 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:64 year:2015 number:5 pages:1819-1832 http://dx.doi.org/10.1109/TVT.2014.2340894 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6860314 http://search.proquest.com/docview/1699221922 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 64 2015 5 1819-1832 |
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10.1109/TVT.2014.2340894 doi PQ20160617 (DE-627)OLC1964446732 (DE-599)GBVOLC1964446732 (PRQ)c2334-52513c57c32ca93dc91d5526b9326ff2c957638427fa70ee301502a2f6e8f8d20 (KEY)0030991520150000064000501819ultrawidebandchannelestimationabayesiancompressive DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Xiantao Cheng verfasserin aut Ultrawideband Channel Estimation: A Bayesian Compressive Sensing Strategy Based on Statistical Sparsity 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier To cope with the formidable sampling rate required by Nyquist criterion, compressive sensing (CS) has been recently adopted for ultrawideband (UWB) channel estimation. In this paper, exploiting the statistical sparsity of real UWB signals in the basis formed by eigenvectors, we develop a new CS dictionary called eigendictionary, which enables the use of CS for UWB channel estimation. With respect to the eigendictionary, the expansion vector of UWB signals is sparse and exhibits an additional structure in the form of statistically significant coefficients occurring in clusters. Capitalizing on this structure, we propose two novel Bayesian CS (BCS) algorithms to efficiently reconstruct UWB signals from a small collection of random projection measurements. Furthermore, by utilizing the common sparsity profile inherent in UWB signals, we extend the proposed Bayesian algorithms to multitask (MT) versions, which can simultaneously recover multiple UWB signals if available. Since the statistical connection between different UWB signals is exploited, the developed MT-BCS can obtain better performance than the single-task version. Extensive simulations using real UWB data show that the proposed schemes considerably reduce the requirement on sampling rate and present excellent performance compared with the traditional correlator and other CS-based channel estimation schemes. signal reconstruction eigenvectors ultra wideband communication eigendictionary ultrawideband channel estimation Vectors Bayesian compressive sensing statistical sparsity expansion vector multiple UWB signals compressed sensing Bayes methods Ultra wideband technology Dictionaries random projection measurements eigenvalues and eigenfunctions common sparsity profile Bayesian algorithms channel estimation Nyquist criterion Receivers Algorithms Sparsity Eigenvectors Bayesian statistical decision theory Technology application Channeling (Physics) Research Usage Mengyao Wang oth Yong Liang Guan oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 64(2015), 5, Seite 1819-1832 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:64 year:2015 number:5 pages:1819-1832 http://dx.doi.org/10.1109/TVT.2014.2340894 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6860314 http://search.proquest.com/docview/1699221922 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 64 2015 5 1819-1832 |
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10.1109/TVT.2014.2340894 doi PQ20160617 (DE-627)OLC1964446732 (DE-599)GBVOLC1964446732 (PRQ)c2334-52513c57c32ca93dc91d5526b9326ff2c957638427fa70ee301502a2f6e8f8d20 (KEY)0030991520150000064000501819ultrawidebandchannelestimationabayesiancompressive DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Xiantao Cheng verfasserin aut Ultrawideband Channel Estimation: A Bayesian Compressive Sensing Strategy Based on Statistical Sparsity 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier To cope with the formidable sampling rate required by Nyquist criterion, compressive sensing (CS) has been recently adopted for ultrawideband (UWB) channel estimation. In this paper, exploiting the statistical sparsity of real UWB signals in the basis formed by eigenvectors, we develop a new CS dictionary called eigendictionary, which enables the use of CS for UWB channel estimation. With respect to the eigendictionary, the expansion vector of UWB signals is sparse and exhibits an additional structure in the form of statistically significant coefficients occurring in clusters. Capitalizing on this structure, we propose two novel Bayesian CS (BCS) algorithms to efficiently reconstruct UWB signals from a small collection of random projection measurements. Furthermore, by utilizing the common sparsity profile inherent in UWB signals, we extend the proposed Bayesian algorithms to multitask (MT) versions, which can simultaneously recover multiple UWB signals if available. Since the statistical connection between different UWB signals is exploited, the developed MT-BCS can obtain better performance than the single-task version. Extensive simulations using real UWB data show that the proposed schemes considerably reduce the requirement on sampling rate and present excellent performance compared with the traditional correlator and other CS-based channel estimation schemes. signal reconstruction eigenvectors ultra wideband communication eigendictionary ultrawideband channel estimation Vectors Bayesian compressive sensing statistical sparsity expansion vector multiple UWB signals compressed sensing Bayes methods Ultra wideband technology Dictionaries random projection measurements eigenvalues and eigenfunctions common sparsity profile Bayesian algorithms channel estimation Nyquist criterion Receivers Algorithms Sparsity Eigenvectors Bayesian statistical decision theory Technology application Channeling (Physics) Research Usage Mengyao Wang oth Yong Liang Guan oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 64(2015), 5, Seite 1819-1832 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:64 year:2015 number:5 pages:1819-1832 http://dx.doi.org/10.1109/TVT.2014.2340894 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6860314 http://search.proquest.com/docview/1699221922 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 64 2015 5 1819-1832 |
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10.1109/TVT.2014.2340894 doi PQ20160617 (DE-627)OLC1964446732 (DE-599)GBVOLC1964446732 (PRQ)c2334-52513c57c32ca93dc91d5526b9326ff2c957638427fa70ee301502a2f6e8f8d20 (KEY)0030991520150000064000501819ultrawidebandchannelestimationabayesiancompressive DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Xiantao Cheng verfasserin aut Ultrawideband Channel Estimation: A Bayesian Compressive Sensing Strategy Based on Statistical Sparsity 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier To cope with the formidable sampling rate required by Nyquist criterion, compressive sensing (CS) has been recently adopted for ultrawideband (UWB) channel estimation. In this paper, exploiting the statistical sparsity of real UWB signals in the basis formed by eigenvectors, we develop a new CS dictionary called eigendictionary, which enables the use of CS for UWB channel estimation. With respect to the eigendictionary, the expansion vector of UWB signals is sparse and exhibits an additional structure in the form of statistically significant coefficients occurring in clusters. Capitalizing on this structure, we propose two novel Bayesian CS (BCS) algorithms to efficiently reconstruct UWB signals from a small collection of random projection measurements. Furthermore, by utilizing the common sparsity profile inherent in UWB signals, we extend the proposed Bayesian algorithms to multitask (MT) versions, which can simultaneously recover multiple UWB signals if available. Since the statistical connection between different UWB signals is exploited, the developed MT-BCS can obtain better performance than the single-task version. Extensive simulations using real UWB data show that the proposed schemes considerably reduce the requirement on sampling rate and present excellent performance compared with the traditional correlator and other CS-based channel estimation schemes. signal reconstruction eigenvectors ultra wideband communication eigendictionary ultrawideband channel estimation Vectors Bayesian compressive sensing statistical sparsity expansion vector multiple UWB signals compressed sensing Bayes methods Ultra wideband technology Dictionaries random projection measurements eigenvalues and eigenfunctions common sparsity profile Bayesian algorithms channel estimation Nyquist criterion Receivers Algorithms Sparsity Eigenvectors Bayesian statistical decision theory Technology application Channeling (Physics) Research Usage Mengyao Wang oth Yong Liang Guan oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 64(2015), 5, Seite 1819-1832 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:64 year:2015 number:5 pages:1819-1832 http://dx.doi.org/10.1109/TVT.2014.2340894 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6860314 http://search.proquest.com/docview/1699221922 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 64 2015 5 1819-1832 |
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Xiantao Cheng ddc 620 bkl 53.70 bkl 53.74 misc signal reconstruction misc eigenvectors misc ultra wideband communication misc eigendictionary misc ultrawideband channel estimation misc Vectors misc Bayesian compressive sensing misc statistical sparsity misc expansion vector misc multiple UWB signals misc compressed sensing misc Bayes methods misc Ultra wideband technology misc Dictionaries misc random projection measurements misc eigenvalues and eigenfunctions misc common sparsity profile misc Bayesian algorithms misc channel estimation misc Nyquist criterion misc Receivers misc Algorithms misc Sparsity misc Eigenvectors misc Bayesian statistical decision theory misc Technology application misc Channeling (Physics) misc Research misc Usage Ultrawideband Channel Estimation: A Bayesian Compressive Sensing Strategy Based on Statistical Sparsity |
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620 DNB 53.70 bkl 53.74 bkl Ultrawideband Channel Estimation: A Bayesian Compressive Sensing Strategy Based on Statistical Sparsity signal reconstruction eigenvectors ultra wideband communication eigendictionary ultrawideband channel estimation Vectors Bayesian compressive sensing statistical sparsity expansion vector multiple UWB signals compressed sensing Bayes methods Ultra wideband technology Dictionaries random projection measurements eigenvalues and eigenfunctions common sparsity profile Bayesian algorithms channel estimation Nyquist criterion Receivers Algorithms Sparsity Eigenvectors Bayesian statistical decision theory Technology application Channeling (Physics) Research Usage |
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Ultrawideband Channel Estimation: A Bayesian Compressive Sensing Strategy Based on Statistical Sparsity |
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To cope with the formidable sampling rate required by Nyquist criterion, compressive sensing (CS) has been recently adopted for ultrawideband (UWB) channel estimation. In this paper, exploiting the statistical sparsity of real UWB signals in the basis formed by eigenvectors, we develop a new CS dictionary called eigendictionary, which enables the use of CS for UWB channel estimation. With respect to the eigendictionary, the expansion vector of UWB signals is sparse and exhibits an additional structure in the form of statistically significant coefficients occurring in clusters. Capitalizing on this structure, we propose two novel Bayesian CS (BCS) algorithms to efficiently reconstruct UWB signals from a small collection of random projection measurements. Furthermore, by utilizing the common sparsity profile inherent in UWB signals, we extend the proposed Bayesian algorithms to multitask (MT) versions, which can simultaneously recover multiple UWB signals if available. Since the statistical connection between different UWB signals is exploited, the developed MT-BCS can obtain better performance than the single-task version. Extensive simulations using real UWB data show that the proposed schemes considerably reduce the requirement on sampling rate and present excellent performance compared with the traditional correlator and other CS-based channel estimation schemes. |
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To cope with the formidable sampling rate required by Nyquist criterion, compressive sensing (CS) has been recently adopted for ultrawideband (UWB) channel estimation. In this paper, exploiting the statistical sparsity of real UWB signals in the basis formed by eigenvectors, we develop a new CS dictionary called eigendictionary, which enables the use of CS for UWB channel estimation. With respect to the eigendictionary, the expansion vector of UWB signals is sparse and exhibits an additional structure in the form of statistically significant coefficients occurring in clusters. Capitalizing on this structure, we propose two novel Bayesian CS (BCS) algorithms to efficiently reconstruct UWB signals from a small collection of random projection measurements. Furthermore, by utilizing the common sparsity profile inherent in UWB signals, we extend the proposed Bayesian algorithms to multitask (MT) versions, which can simultaneously recover multiple UWB signals if available. Since the statistical connection between different UWB signals is exploited, the developed MT-BCS can obtain better performance than the single-task version. Extensive simulations using real UWB data show that the proposed schemes considerably reduce the requirement on sampling rate and present excellent performance compared with the traditional correlator and other CS-based channel estimation schemes. |
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To cope with the formidable sampling rate required by Nyquist criterion, compressive sensing (CS) has been recently adopted for ultrawideband (UWB) channel estimation. In this paper, exploiting the statistical sparsity of real UWB signals in the basis formed by eigenvectors, we develop a new CS dictionary called eigendictionary, which enables the use of CS for UWB channel estimation. With respect to the eigendictionary, the expansion vector of UWB signals is sparse and exhibits an additional structure in the form of statistically significant coefficients occurring in clusters. Capitalizing on this structure, we propose two novel Bayesian CS (BCS) algorithms to efficiently reconstruct UWB signals from a small collection of random projection measurements. Furthermore, by utilizing the common sparsity profile inherent in UWB signals, we extend the proposed Bayesian algorithms to multitask (MT) versions, which can simultaneously recover multiple UWB signals if available. Since the statistical connection between different UWB signals is exploited, the developed MT-BCS can obtain better performance than the single-task version. Extensive simulations using real UWB data show that the proposed schemes considerably reduce the requirement on sampling rate and present excellent performance compared with the traditional correlator and other CS-based channel estimation schemes. |
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