| 基于LSTM与多特征融合的高铁无线信道场景识别 |
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| 引用本文： | 王英捷,周涛,陶成. 基于LSTM与多特征融合的高铁无线信道场景识别[J]. 电波科学学报, 2021, 36(3): 453-459,476. DOI: 10.12265/j.cjors.2020050 |
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| 作者姓名： | 王英捷 周涛 陶成 |
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| 作者单位： | 北京交通大学电子信息工程学院，北京 100044 |
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| 摘 要： | 为满足5G移动通信系统中用户通信业务质量的需求,提出了 一种基于长短时记忆(long short term memory,LSTM)与多特征融合的识别方法准确识别高铁无线信道场景,该方法能够与智能决策系统相结合,提高通信系统的整体性能.首先,对不同信道场景的特点及信道特征参数进行阐述,并对整体数据集进行训练集与测试集的...
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| 关 键 词： | 高铁无线信道 信道场景识别 多特征融合 长短时记忆(LSTM)神经网络 混淆矩阵 受试者工作特征(ROC)曲线 |
| 收稿时间： | 2020-03-29 |
Multi-feature fusion for high-speed railway propagation scene recognition based on LSTM networks |
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| WANG Yingjie,ZHOU Tao,TAO Cheng. Multi-feature fusion for high-speed railway propagation scene recognition based on LSTM networks[J]. Chinese Journal of Radio Science, 2021, 36(3): 453-459,476. DOI: 10.12265/j.cjors.2020050 |
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| Authors: | WANG Yingjie ZHOU Tao TAO Cheng |
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| Affiliation: | School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China |
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| Abstract: | In order to fulfill the requirement of service quality of the 5G system, we consider multi-feature fusion methods in this paper to recognize the propagation scene of high-speed railway precisely. After that, the performance of system is improved by using some adaptive technologies. Firstly, we make an explanation on propagation scenes and channel feature parameters, and the dataset is split into two sets: training set and testing set. Then we propose a multilayer long short term memory(LSTM) architecture with a novel weighted score fusion scheme to learn classification from different propagation scenes and compare the results with usage of three regular fusion schemes. The result shows that the recognition accuracy of proposed model on testing dataset reaches 92.2%, the area under curve(AUC) of this model is better than the other three fusion schemes. Therefore, the proposed method provides an accurate recognition application for high-speed railway communication systems. |
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