- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | CHEN Jianyi, YAN Lianshan, GUO Xinghai, ZHONG Zhangdui. 5G Antenna Parameter Planning for Intelligent Marshalling Station Based on Machine Learning Algorithm[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20220329
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The 5th generation mobile communication technology (5G) has advantages such as a high connection rate and large system capacity, which can support the development of marshalling station communication systems. However, the 5G antenna parameter planning is challenging due to the large amount of calculation, and it is difficult to achieve both high efficiency and accuracy simultaneously. Therefore, Based on the CloudRT ray-tracing (RT) platform, the signal coverage scenario was simulated. By considering the problem of angle selection and power optimization of communication base station antenna, a planning method based on a machine learning algorithm was proposed. Firstly, based on the overlap complexity and the clustering algorithm, the antenna angle parameters were clustered, and the clustering results were evaluated. Secondly, according to the relationship between antenna gain and angle, the optimization algorithm was designed to simplify the selection process of antenna angle parameter combinations. Finally, the simulated annealing operator was introduced into the genetic algorithm to solve the optimal power combination, and Jiangcun Marshalling Station was taken as the scenario for verification. The results indicate that the total power derived by the proposed method is 5.6 dB higher than that of the traversal algorithm, and the time required is only 13.5% of the traversal algorithm. It achieves high efficiency and accuracy simultaneously, which is expected to be applied to the 5G system of high-speed railways and marshalling stations.
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Figures(9) / Tables(8)
QIAO Qingfeng, YANG Weidong, ZHU Qi, CHEN Guangxiong. Generation Mechanism of Railway Disc Brake Squeal and Its Suppression Method[J]. Journal of Southwest Jiaotong University, 2021, 56(1): 62-67. doi: 10.3969/j.issn.0258-2724.20190799
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