A Sparsity Adaptive Algorithm to Recover NB-IoT Signal From Legacy LTE Interference

被引：5
作者：
Guo, Yijia [1 ]
Wen, Wenkun [2 ]
Wu, Peiran [1 ]
Xia, Minghua [1 ]
机构：
[1] Sun Yat Sen Univ, Sch Elect & Informat Technol, Guangzhou 510006, Peoples R China
[2] Guangzhou Techphant Co Ltd, R&D Dept, Guangzhou 510310, Peoples R China
来源：
IEEE WIRELESS COMMUNICATIONS LETTERS | 2021年 / 10卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Terms-K-means clustering; LTE interference; narrow-band Internet of Things; sparse recovery; PURSUIT;
D O I：
10.1109/LWC.2021.3112791
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
As a forerunner in 5G technologies, Narrowband Internet of Things (NB-IoT) will be inevitably coexisting with the legacy Long-Term Evolution (LTE) system. Thus, it is imperative for NB-IoT to mitigate LTE interference. By virtue of the strong temporal correlation of the NB-IoT signal, this letter develops a sparsity adaptive algorithm to recover the NB-IoT signal from legacy LTE interference, by combining K-means clustering and sparsity adaptive matching pursuit (SAMP). In particular, the support of the NB-IoT signal is first estimated coarsely by K-means clustering and SAMP algorithm without sparsity limitation. Then, the estimated support is refined by a repeat mechanism. Simulation results demonstrate the effectiveness of the developed algorithm in terms of recovery probability and bit error rate, compared with competing algorithms.
引用
收藏
页码：2703 / 2707
页数：5
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