中图分类号：TN929.5 文献标志码：A DOI: 10.16157/j.issn.0258-7998.245465
中文引用格式： 胡荣青，张成挺，任国伟，等. 基于深度残差神经网络的5G信号室内分布预测[J]. 电子技术应用，2024，50(12)：77-81.
英文引用格式： Hu Rongqing，Zhang Chengting，Ren Guowei，et al. Prediction of indoor 5G signal distribution based on deep residual neural network model[J]. Application of Electronic Technique，2024，50(12)：77-81.

Prediction of indoor 5G signal distribution based on deep residual neural network model

Abstract： In order to solve the problem of the quality and stability of indoor signal coverage of the fifth generation mobile communication technology (5G), a new indoor 5G signal distribution prediction method is proposed in this paper. A depth residual neural network model based on the full connection layer is adopted. The three-dimensional spatial coordinate information of transmitter and receiver and Reference Signal Receiving Power (RSRP) data of receiver are used as input features, without the need to collect complex environmental features. The experimental results show that the depth residual neural network model can effectively predict the indoor 5G signal distribution with MAE of 0.029 455 and RMSE of 0.041 495 compared with the actual value after normalized training without relying on detailed environmental parameters. This study confirms the effectiveness of the prediction method based on deep residual neural network in indoor 5G signal coverage prediction, which provides scientific basis and technical means for optimizing indoor 5G network deployment and improving user experience, and has important practical application value.

Key words : deep residual neural network；signal distribution；signal strength；signal prediction；RSRP