Block Sparse Bayesian Learning-Based NB-IoT Interference Elimination in LTE-Advanced Systems
Narrowband Internet-of-Things (NB-IoT) is one of the emerging 5G technologies, but might introduce narrowband interference (NBI) to existing broadband systems, such as long-term evolution advanced (LTE-A) systems. Thus, the mitigation of the NB-IoT interference to LTE-A is an important issue for the...
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| Autor*in: |
Liu, Sicong [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on communications - New York, NY : IEEE, 1972, 65(2017), 10, Seite 4559-4571 |
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| Übergeordnetes Werk: |
volume:65 ; year:2017 ; number:10 ; pages:4559-4571 |
| Links: |
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| DOI / URN: |
10.1109/TCOMM.2017.2723572 |
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| 520 | |a Narrowband Internet-of-Things (NB-IoT) is one of the emerging 5G technologies, but might introduce narrowband interference (NBI) to existing broadband systems, such as long-term evolution advanced (LTE-A) systems. Thus, the mitigation of the NB-IoT interference to LTE-A is an important issue for the harmonic coexistence and compatibility between 4G and 5G. In this paper, a newly emerged sparse approximation technique, block sparse Bayesian learning (BSBL), is utilized to estimate the NB-IoT interference in LTE-A systems. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach, and the BSBL theory is utilized to recover the practical block sparse NBI. A BSBL-based method, partition estimated BSBL, is proposed. With the aid of the estimated block partition beforehand, the Bayesian parameters are obtained to yield the NBI estimation. The intra-block correlation (IBC) is considered to facilitate the recovery. Moreover, exploiting the inherent structure of the identical IBC matrix, another method of informative BSBL is proposed to further improve the accuracy, which does not require prior estimation of the block partition. Reported simulation results demonstrate that the proposed methods are effective in canceling the NB-IoT interference in LTE-A systems, and significantly outperform other conventional methods. | ||
| 650 | 4 | |a Covariance matrices | |
| 650 | 4 | |a narrowband interference | |
| 650 | 4 | |a long term evolution advanced | |
| 650 | 4 | |a Bayes methods | |
| 650 | 4 | |a OFDM | |
| 650 | 4 | |a temporal differential measuring | |
| 650 | 4 | |a Broadband communication | |
| 650 | 4 | |a Narrowband | |
| 650 | 4 | |a Estimation | |
| 650 | 4 | |a Interference | |
| 650 | 4 | |a Narrowband Internet-of-Things | |
| 650 | 4 | |a block sparse Bayesian learning | |
| 700 | 1 | |a Yang, Fang |4 oth | |
| 700 | 1 | |a Song, Jian |4 oth | |
| 700 | 1 | |a Han, Zhu |4 oth | |
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10.1109/TCOMM.2017.2723572 doi PQ20171125 (DE-627)OLC199716809X (DE-599)GBVOLC199716809X (PRQ)i656-153a399bb2bd84bd3f2f127c5f8018f3eb303e984fe0ef9bb0f9b8e8daa492fc0 (KEY)0043613520170000065001004559blocksparsebayesianlearningbasednbiotinterferencee DE-627 ger DE-627 rakwb eng 620 DE-600 SA 5540 AVZ rvk Liu, Sicong verfasserin aut Block Sparse Bayesian Learning-Based NB-IoT Interference Elimination in LTE-Advanced Systems 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Narrowband Internet-of-Things (NB-IoT) is one of the emerging 5G technologies, but might introduce narrowband interference (NBI) to existing broadband systems, such as long-term evolution advanced (LTE-A) systems. Thus, the mitigation of the NB-IoT interference to LTE-A is an important issue for the harmonic coexistence and compatibility between 4G and 5G. In this paper, a newly emerged sparse approximation technique, block sparse Bayesian learning (BSBL), is utilized to estimate the NB-IoT interference in LTE-A systems. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach, and the BSBL theory is utilized to recover the practical block sparse NBI. A BSBL-based method, partition estimated BSBL, is proposed. With the aid of the estimated block partition beforehand, the Bayesian parameters are obtained to yield the NBI estimation. The intra-block correlation (IBC) is considered to facilitate the recovery. Moreover, exploiting the inherent structure of the identical IBC matrix, another method of informative BSBL is proposed to further improve the accuracy, which does not require prior estimation of the block partition. Reported simulation results demonstrate that the proposed methods are effective in canceling the NB-IoT interference in LTE-A systems, and significantly outperform other conventional methods. Covariance matrices narrowband interference long term evolution advanced Bayes methods OFDM temporal differential measuring Broadband communication Narrowband Estimation Interference Narrowband Internet-of-Things block sparse Bayesian learning Yang, Fang oth Song, Jian oth Han, Zhu oth Enthalten in IEEE transactions on communications New York, NY : IEEE, 1972 65(2017), 10, Seite 4559-4571 (DE-627)129300624 (DE-600)121987-X (DE-576)014493063 0090-6778 nnns volume:65 year:2017 number:10 pages:4559-4571 http://dx.doi.org/10.1109/TCOMM.2017.2723572 Volltext http://ieeexplore.ieee.org/document/7968406 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2004 SA 5540 AR 65 2017 10 4559-4571 |
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10.1109/TCOMM.2017.2723572 doi PQ20171125 (DE-627)OLC199716809X (DE-599)GBVOLC199716809X (PRQ)i656-153a399bb2bd84bd3f2f127c5f8018f3eb303e984fe0ef9bb0f9b8e8daa492fc0 (KEY)0043613520170000065001004559blocksparsebayesianlearningbasednbiotinterferencee DE-627 ger DE-627 rakwb eng 620 DE-600 SA 5540 AVZ rvk Liu, Sicong verfasserin aut Block Sparse Bayesian Learning-Based NB-IoT Interference Elimination in LTE-Advanced Systems 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Narrowband Internet-of-Things (NB-IoT) is one of the emerging 5G technologies, but might introduce narrowband interference (NBI) to existing broadband systems, such as long-term evolution advanced (LTE-A) systems. Thus, the mitigation of the NB-IoT interference to LTE-A is an important issue for the harmonic coexistence and compatibility between 4G and 5G. In this paper, a newly emerged sparse approximation technique, block sparse Bayesian learning (BSBL), is utilized to estimate the NB-IoT interference in LTE-A systems. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach, and the BSBL theory is utilized to recover the practical block sparse NBI. A BSBL-based method, partition estimated BSBL, is proposed. With the aid of the estimated block partition beforehand, the Bayesian parameters are obtained to yield the NBI estimation. The intra-block correlation (IBC) is considered to facilitate the recovery. Moreover, exploiting the inherent structure of the identical IBC matrix, another method of informative BSBL is proposed to further improve the accuracy, which does not require prior estimation of the block partition. Reported simulation results demonstrate that the proposed methods are effective in canceling the NB-IoT interference in LTE-A systems, and significantly outperform other conventional methods. Covariance matrices narrowband interference long term evolution advanced Bayes methods OFDM temporal differential measuring Broadband communication Narrowband Estimation Interference Narrowband Internet-of-Things block sparse Bayesian learning Yang, Fang oth Song, Jian oth Han, Zhu oth Enthalten in IEEE transactions on communications New York, NY : IEEE, 1972 65(2017), 10, Seite 4559-4571 (DE-627)129300624 (DE-600)121987-X (DE-576)014493063 0090-6778 nnns volume:65 year:2017 number:10 pages:4559-4571 http://dx.doi.org/10.1109/TCOMM.2017.2723572 Volltext http://ieeexplore.ieee.org/document/7968406 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2004 SA 5540 AR 65 2017 10 4559-4571 |
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10.1109/TCOMM.2017.2723572 doi PQ20171125 (DE-627)OLC199716809X (DE-599)GBVOLC199716809X (PRQ)i656-153a399bb2bd84bd3f2f127c5f8018f3eb303e984fe0ef9bb0f9b8e8daa492fc0 (KEY)0043613520170000065001004559blocksparsebayesianlearningbasednbiotinterferencee DE-627 ger DE-627 rakwb eng 620 DE-600 SA 5540 AVZ rvk Liu, Sicong verfasserin aut Block Sparse Bayesian Learning-Based NB-IoT Interference Elimination in LTE-Advanced Systems 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Narrowband Internet-of-Things (NB-IoT) is one of the emerging 5G technologies, but might introduce narrowband interference (NBI) to existing broadband systems, such as long-term evolution advanced (LTE-A) systems. Thus, the mitigation of the NB-IoT interference to LTE-A is an important issue for the harmonic coexistence and compatibility between 4G and 5G. In this paper, a newly emerged sparse approximation technique, block sparse Bayesian learning (BSBL), is utilized to estimate the NB-IoT interference in LTE-A systems. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach, and the BSBL theory is utilized to recover the practical block sparse NBI. A BSBL-based method, partition estimated BSBL, is proposed. With the aid of the estimated block partition beforehand, the Bayesian parameters are obtained to yield the NBI estimation. The intra-block correlation (IBC) is considered to facilitate the recovery. Moreover, exploiting the inherent structure of the identical IBC matrix, another method of informative BSBL is proposed to further improve the accuracy, which does not require prior estimation of the block partition. Reported simulation results demonstrate that the proposed methods are effective in canceling the NB-IoT interference in LTE-A systems, and significantly outperform other conventional methods. Covariance matrices narrowband interference long term evolution advanced Bayes methods OFDM temporal differential measuring Broadband communication Narrowband Estimation Interference Narrowband Internet-of-Things block sparse Bayesian learning Yang, Fang oth Song, Jian oth Han, Zhu oth Enthalten in IEEE transactions on communications New York, NY : IEEE, 1972 65(2017), 10, Seite 4559-4571 (DE-627)129300624 (DE-600)121987-X (DE-576)014493063 0090-6778 nnns volume:65 year:2017 number:10 pages:4559-4571 http://dx.doi.org/10.1109/TCOMM.2017.2723572 Volltext http://ieeexplore.ieee.org/document/7968406 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2004 SA 5540 AR 65 2017 10 4559-4571 |
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10.1109/TCOMM.2017.2723572 doi PQ20171125 (DE-627)OLC199716809X (DE-599)GBVOLC199716809X (PRQ)i656-153a399bb2bd84bd3f2f127c5f8018f3eb303e984fe0ef9bb0f9b8e8daa492fc0 (KEY)0043613520170000065001004559blocksparsebayesianlearningbasednbiotinterferencee DE-627 ger DE-627 rakwb eng 620 DE-600 SA 5540 AVZ rvk Liu, Sicong verfasserin aut Block Sparse Bayesian Learning-Based NB-IoT Interference Elimination in LTE-Advanced Systems 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Narrowband Internet-of-Things (NB-IoT) is one of the emerging 5G technologies, but might introduce narrowband interference (NBI) to existing broadband systems, such as long-term evolution advanced (LTE-A) systems. Thus, the mitigation of the NB-IoT interference to LTE-A is an important issue for the harmonic coexistence and compatibility between 4G and 5G. In this paper, a newly emerged sparse approximation technique, block sparse Bayesian learning (BSBL), is utilized to estimate the NB-IoT interference in LTE-A systems. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach, and the BSBL theory is utilized to recover the practical block sparse NBI. A BSBL-based method, partition estimated BSBL, is proposed. With the aid of the estimated block partition beforehand, the Bayesian parameters are obtained to yield the NBI estimation. The intra-block correlation (IBC) is considered to facilitate the recovery. Moreover, exploiting the inherent structure of the identical IBC matrix, another method of informative BSBL is proposed to further improve the accuracy, which does not require prior estimation of the block partition. Reported simulation results demonstrate that the proposed methods are effective in canceling the NB-IoT interference in LTE-A systems, and significantly outperform other conventional methods. Covariance matrices narrowband interference long term evolution advanced Bayes methods OFDM temporal differential measuring Broadband communication Narrowband Estimation Interference Narrowband Internet-of-Things block sparse Bayesian learning Yang, Fang oth Song, Jian oth Han, Zhu oth Enthalten in IEEE transactions on communications New York, NY : IEEE, 1972 65(2017), 10, Seite 4559-4571 (DE-627)129300624 (DE-600)121987-X (DE-576)014493063 0090-6778 nnns volume:65 year:2017 number:10 pages:4559-4571 http://dx.doi.org/10.1109/TCOMM.2017.2723572 Volltext http://ieeexplore.ieee.org/document/7968406 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2004 SA 5540 AR 65 2017 10 4559-4571 |
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10.1109/TCOMM.2017.2723572 doi PQ20171125 (DE-627)OLC199716809X (DE-599)GBVOLC199716809X (PRQ)i656-153a399bb2bd84bd3f2f127c5f8018f3eb303e984fe0ef9bb0f9b8e8daa492fc0 (KEY)0043613520170000065001004559blocksparsebayesianlearningbasednbiotinterferencee DE-627 ger DE-627 rakwb eng 620 DE-600 SA 5540 AVZ rvk Liu, Sicong verfasserin aut Block Sparse Bayesian Learning-Based NB-IoT Interference Elimination in LTE-Advanced Systems 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Narrowband Internet-of-Things (NB-IoT) is one of the emerging 5G technologies, but might introduce narrowband interference (NBI) to existing broadband systems, such as long-term evolution advanced (LTE-A) systems. Thus, the mitigation of the NB-IoT interference to LTE-A is an important issue for the harmonic coexistence and compatibility between 4G and 5G. In this paper, a newly emerged sparse approximation technique, block sparse Bayesian learning (BSBL), is utilized to estimate the NB-IoT interference in LTE-A systems. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach, and the BSBL theory is utilized to recover the practical block sparse NBI. A BSBL-based method, partition estimated BSBL, is proposed. With the aid of the estimated block partition beforehand, the Bayesian parameters are obtained to yield the NBI estimation. The intra-block correlation (IBC) is considered to facilitate the recovery. Moreover, exploiting the inherent structure of the identical IBC matrix, another method of informative BSBL is proposed to further improve the accuracy, which does not require prior estimation of the block partition. Reported simulation results demonstrate that the proposed methods are effective in canceling the NB-IoT interference in LTE-A systems, and significantly outperform other conventional methods. Covariance matrices narrowband interference long term evolution advanced Bayes methods OFDM temporal differential measuring Broadband communication Narrowband Estimation Interference Narrowband Internet-of-Things block sparse Bayesian learning Yang, Fang oth Song, Jian oth Han, Zhu oth Enthalten in IEEE transactions on communications New York, NY : IEEE, 1972 65(2017), 10, Seite 4559-4571 (DE-627)129300624 (DE-600)121987-X (DE-576)014493063 0090-6778 nnns volume:65 year:2017 number:10 pages:4559-4571 http://dx.doi.org/10.1109/TCOMM.2017.2723572 Volltext http://ieeexplore.ieee.org/document/7968406 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2004 SA 5540 AR 65 2017 10 4559-4571 |
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Thus, the mitigation of the NB-IoT interference to LTE-A is an important issue for the harmonic coexistence and compatibility between 4G and 5G. In this paper, a newly emerged sparse approximation technique, block sparse Bayesian learning (BSBL), is utilized to estimate the NB-IoT interference in LTE-A systems. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach, and the BSBL theory is utilized to recover the practical block sparse NBI. A BSBL-based method, partition estimated BSBL, is proposed. With the aid of the estimated block partition beforehand, the Bayesian parameters are obtained to yield the NBI estimation. The intra-block correlation (IBC) is considered to facilitate the recovery. Moreover, exploiting the inherent structure of the identical IBC matrix, another method of informative BSBL is proposed to further improve the accuracy, which does not require prior estimation of the block partition. 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Block Sparse Bayesian Learning-Based NB-IoT Interference Elimination in LTE-Advanced Systems |
| abstract |
Narrowband Internet-of-Things (NB-IoT) is one of the emerging 5G technologies, but might introduce narrowband interference (NBI) to existing broadband systems, such as long-term evolution advanced (LTE-A) systems. Thus, the mitigation of the NB-IoT interference to LTE-A is an important issue for the harmonic coexistence and compatibility between 4G and 5G. In this paper, a newly emerged sparse approximation technique, block sparse Bayesian learning (BSBL), is utilized to estimate the NB-IoT interference in LTE-A systems. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach, and the BSBL theory is utilized to recover the practical block sparse NBI. A BSBL-based method, partition estimated BSBL, is proposed. With the aid of the estimated block partition beforehand, the Bayesian parameters are obtained to yield the NBI estimation. The intra-block correlation (IBC) is considered to facilitate the recovery. Moreover, exploiting the inherent structure of the identical IBC matrix, another method of informative BSBL is proposed to further improve the accuracy, which does not require prior estimation of the block partition. Reported simulation results demonstrate that the proposed methods are effective in canceling the NB-IoT interference in LTE-A systems, and significantly outperform other conventional methods. |
| abstractGer |
Narrowband Internet-of-Things (NB-IoT) is one of the emerging 5G technologies, but might introduce narrowband interference (NBI) to existing broadband systems, such as long-term evolution advanced (LTE-A) systems. Thus, the mitigation of the NB-IoT interference to LTE-A is an important issue for the harmonic coexistence and compatibility between 4G and 5G. In this paper, a newly emerged sparse approximation technique, block sparse Bayesian learning (BSBL), is utilized to estimate the NB-IoT interference in LTE-A systems. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach, and the BSBL theory is utilized to recover the practical block sparse NBI. A BSBL-based method, partition estimated BSBL, is proposed. With the aid of the estimated block partition beforehand, the Bayesian parameters are obtained to yield the NBI estimation. The intra-block correlation (IBC) is considered to facilitate the recovery. Moreover, exploiting the inherent structure of the identical IBC matrix, another method of informative BSBL is proposed to further improve the accuracy, which does not require prior estimation of the block partition. Reported simulation results demonstrate that the proposed methods are effective in canceling the NB-IoT interference in LTE-A systems, and significantly outperform other conventional methods. |
| abstract_unstemmed |
Narrowband Internet-of-Things (NB-IoT) is one of the emerging 5G technologies, but might introduce narrowband interference (NBI) to existing broadband systems, such as long-term evolution advanced (LTE-A) systems. Thus, the mitigation of the NB-IoT interference to LTE-A is an important issue for the harmonic coexistence and compatibility between 4G and 5G. In this paper, a newly emerged sparse approximation technique, block sparse Bayesian learning (BSBL), is utilized to estimate the NB-IoT interference in LTE-A systems. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach, and the BSBL theory is utilized to recover the practical block sparse NBI. A BSBL-based method, partition estimated BSBL, is proposed. With the aid of the estimated block partition beforehand, the Bayesian parameters are obtained to yield the NBI estimation. The intra-block correlation (IBC) is considered to facilitate the recovery. Moreover, exploiting the inherent structure of the identical IBC matrix, another method of informative BSBL is proposed to further improve the accuracy, which does not require prior estimation of the block partition. Reported simulation results demonstrate that the proposed methods are effective in canceling the NB-IoT interference in LTE-A systems, and significantly outperform other conventional methods. |
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