5G Antenna Parameter Planning for Intelligent Marshalling Station Based on Machine Learning Algorithm

CHEN Jianyi; YAN Lianshan; GUO Xinghai; ZHONG Zhangdui

doi:10.3969/j.issn.0258-2724.20220329

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Journal of Southwest Jiaotong University > 2024 > Accepted Manuscript

ZHUANG Jiawei, DIAO Yongfa, ZHANG Li’an, SHEN Henggen. Evolution Characteristics of Indoor Pollutant Concentration in Buoyancy-Driven Natural Ventilation[J]. Journal of Southwest Jiaotong University, 2021, 56(1): 47-55. doi: 10.3969/j.issn.0258-2724.20190582

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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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5G Antenna Parameter Planning for Intelligent Marshalling Station Based on Machine Learning Algorithm

doi: 10.3969/j.issn.0258-2724.20220329

CHEN Jianyi^{1, 2
,},
YAN Lianshan^{1
,
,},
GUO Xinghai³,
ZHONG Zhangdui^{3, 4}

1.
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
2.
China Railway Guangzhou Bureau Co. Ltd., Guangzhou 510088, China
3.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
4.
Key Laboratory of Railway Industry of Broadband Mobile Information Communications, Beijing Jiaotong University, Beijing 100044, China

Received Date: 13 May 2022
Rev Recd Date: 02 Aug 2022

Available Online: 16 Dec 2024

Abstract

Abstract

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.
- 5G,
- marshalling station,
- machine learning,
- clustering algorithm,
- hybrid genetic algorithm

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References

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