# Fast Optimization of the Installation Position of 5G-R Antenna on the Train Roof
## 1 Introduction
Therefore, when using electromagnetic simulation methods for research, new methods are urgently needed to replace a large number of repeated simulations and multi-parameter effects, so as to effectively replace the complicated electromagnetic simulation work. When using the electromagnetic simulation method to study and analyze the installation position of the roof antenna, the most important step is to accurately model the roof and the antenna. However, due to the large roof area and the variable installation position of the roof antenna, such complicated simulation work will consume a lot of time and occupy a large amount of computer storage space, and, finally, will seriously affect the efficiency of research and analysis. As more practical simulation parameters are applied in the future, such as roof obstacles, material parameters, and the influence of the surrounding environment, the shortcomings of this electromagnetic simulation method will become increasingly prominent. In recent years, with the continuous development of algorithms such as machine learning (ML) and deep learning (DL) in artificial intelligence (AI), many scientific research problems have been successfully solved. When ML algorithms are used to solve electromagnetic problems, the number of repeated electromagnetic simulations and the occupied storage space will be greatly reduced. For example, a fast prediction model for the electromagnetic sensitivity of the balise transmission module (BTM) system based on a BP neural network (BPNN) algorithm is established in [18]. BPNNs have the advantage of being computationally simple and easy to use, but they also have the obvious disadvantage where the network training is prone to become stuck in local minima and the learning process converges slowly. In order to solve this problem, other algorithms can be used to  
Figure 1: A simplified model for the coupling coefficient of pantograph arcing and GSM-R antenna. replace or combine the global optimization algorithm and BP algorithm, such as the ELM algorithm or MEA-BP algorithm. Both the MEA-BPNN algorithm and ELM algorithm can be used to predict EMC problems. The MEA-BPNN algorithm was used to establish a current distribution prediction controller, which significantly reduced the phenomenon of various current components of the motor when the inductance parameters were mismatched [19]. The ELM algorithm was applied to establish a fast prediction model for the radiation field of pantograph arcing, and, finally, the validity and accuracy of the prediction model was verified [20]. A coupling coefficient prediction model based on the multi-module neural network (MMNN) is proposed in this paper. Firstly, a simulation model of the coupling coefficient between the pantograph arcing and the roof antenna (a monopole antenna operating frequency of 2 GHz) was established to construct the dataset. It is also verified that the new installation position (the lowest coupling coefficient) can significantly reduce the influence of EMI caused by pantograph arcing, so as to optimize the installation position of the roof antenna. Then, on the basis of the preliminary analysis of the data change rule, the sub-module prediction model is established by the MEA-BP algorithm and the PSO-ELM algorithm, respectively. By setting the integrated strategy of piecewise prediction, the prediction results are optimized and the accuracy of the prediction model based on the MMNN is further improved. Finally, the prediction model is proven to be able to replace the complicated electromagnetic simulation work accurately and efficiently by a variety of prediction performance indices, which provides an effective prediction method for the rapid optimization of the installation position of the roof antenna. The rest of this paper consists of three sections. In Section 2, data on the coupling coefficients are collected and analyzed. In Section 3, a prediction model of the coupling coefficient based on the MMNN is constructed and its accuracy and efficiency are verified.