A survey on improving the wireless communication with adaptive antenna selection method

被引：20
作者：
Wu, ChienHsiang [1 ]
Lai, Chin-Feng [1 ]
机构：
[1] Natl Cheng Kung Univ, Tainan 70101, Taiwan
来源：
COMPUTER COMMUNICATIONS | 2022年 / 181卷
关键词：
Adaptive antenna; Beamforming; Diversity antenna; Enhance wireless transmission efficiency; Phased array antenna; BEAM SELECTION; DESIGN; OPTIMIZATION; ARCHITECTURE; ACCESS; ANALOG;
D O I：
10.1016/j.comcom.2021.10.034
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Transmission applications in wireless networks have brought unprecedented demands. The demand for high-performance wireless transmission is increasing day by day. Antenna technology is an indispensable part of the development of wireless communication. One potential solution is to resort t intelligent learning techniques to help breakthroughs in the limited antenna technical field. It is based on an adaptive antenna using intelligent learning. It has laid the foundation for signal strength adjustment to enhance wireless transmission efficiency. This paper evaluates the most advanced literature and techniques. A comprehensive description from different perspectives covers several adaptive antenna structures, including diversity antennas, phased array antennas, and beamforming specific learning methods. After that, this paper divides it into different categories, from intelligent learning algorithms and feature data perspectives in a different light to analyze and discuss. This article expects to help readers understand the latest intelligent technology based on adaptive antennas. Further, it sheds novel light on future research directions to meet the development needs of adaptive antennas for future wireless networks.
引用
收藏
页码：374 / 403
页数：30
相关论文
共 166 条
[81]  
Liu K., 2018, IEEE CONF COMPUT
[82]   Direction-of-Arrival Estimation Based on Deep Neural Networks With Robustness to Array Imperfections [J].
Liu, Zhang-Meng ;
Zhang, Chenwei ;
Yu, Philip S. .
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2018, 66 (12) :7315-7327
[83]  
Lizarraga Enrique M., 2019, 2019 XVIII Workshop on Information Processing and Control (RPIC), P253, DOI 10.1109/RPIC.2019.8882140
[84]  
Louppe G., 2014, Understanding random forests: From theory to practice
[85]  
Lovato R, 2019, IEEE ANTENNAS PROP, P1247, DOI [10.1109/APUSNCURSINRSM.2019.8888573, 10.1109/apusncursinrsm.2019.8888573]
[86]   Machine Learning for Beam Alignment in Millimeter Wave Massive MIMO [J].
Ma, Wenyan ;
Qi, Chenhao ;
Li, Geoffrey Ye .
IEEE WIRELESS COMMUNICATIONS LETTERS, 2020, 9 (06) :875-878
[87]  
Malajner M, 2019, INT CONF SYST SIGNAL, P133, DOI [10.1109/iwssip.2019.8787321, 10.1109/IWSSIP.2019.8787321]
[88]  
Matsumura N, 2018 IEEE INT WORKSH
[89]  
Mc Caffrey C, 2017, IEEE SENSOR, P1599
[90]  
Mendki P., 2018, 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU), P1, DOI DOI 10.1109/IOT-SIU.2018.8519852
45678910111213