Exploiting Multiple Antennas for Cognitive Ambient Backscatter Communication

被引:99
|
作者
Guo, Huayan [1 ,2 ]
Zhang, Qianqian [1 ,2 ]
Xiao, Sa [1 ,2 ]
Liang, Ying-Chang [2 ]
机构
[1] Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 611731, Sichuan, Peoples R China
[2] Univ Elect Sci & Technol China, CINC, Chengdu 611731, Sichuan, Peoples R China
来源
IEEE INTERNET OF THINGS JOURNAL | 2019年 / 6卷 / 01期
基金
中国国家自然科学基金;
关键词
Ambient backscatter communications (AmBCs); clustering; cognitive radio; interference cancelation; multiple antennas; statistical machine learning; BLIND CHANNEL ESTIMATION; PERFORMANCE ANALYSIS; INTERNET; THINGS; ENERGY; RADIO;
D O I
10.1109/JIOT.2018.2856633
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cognitive ambient backscatter communication is a novel spectrum sharing paradigm, in which the backscatter system shares not only the same spectrum, but also the same radio-frequency source with the legacy system. Conventional energy detector (ED) suffers from severe error floor problem due to the existence of co-channel direct link interference (DLI) from the legacy system. In this paper, novel error-floor-free detectors are proposed to tackle the DLI using multiple receive antennas at the reader. First, beamforming-assisted ED and likelihood-ratio-based detector are proposed for backscatter symbol detection when the reader has perfect channel state information (CSI). Then a novel statistical clustering framework is proposed for joint CSI feature learning and backscatter symbol detection. Extensive simulation results have shown that the proposed methods can significantly outperform the conventional ED. In addition, the proposed clustering-based methods perform comparably as their counterparts with perfect CSI.
引用
收藏
页码:765 / 775
页数:11
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