Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/208003
Title: Block Sparse Bayesian Learning-Based NB-IoT Interference Elimination in LTE-Advanced Systems
Authors: Sicong Liu;Fang Yang;Jian Song;Zhu Han
Year: 2017
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/208003
volume: 65
issue: 10
More Information: 4559,
4571
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7968406.pdf1.37 MBAdobe PDF
Title: Block Sparse Bayesian Learning-Based NB-IoT Interference Elimination in LTE-Advanced Systems
Authors: Sicong Liu;Fang Yang;Jian Song;Zhu Han
Year: 2017
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/208003
volume: 65
issue: 10
More Information: 4559,
4571
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7968406.pdf1.37 MBAdobe PDF
Title: Block Sparse Bayesian Learning-Based NB-IoT Interference Elimination in LTE-Advanced Systems
Authors: Sicong Liu;Fang Yang;Jian Song;Zhu Han
Year: 2017
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/208003
volume: 65
issue: 10
More Information: 4559,
4571
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7968406.pdf1.37 MBAdobe PDF