| YANG Lihua(杨丽花)*,WANG Zenghao*,ZHANG Jie*,JIANG Ting**.[J].高技术通讯(英文),2022,28(2):115~121 |
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| Deep learning based Doppler frequency offset estimation for 5G-NR downlink in HSR scenario |
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| DOI：10.3772/j.issn.1006-6748.2022.02.001 |
| 中文关键词: |
| 英文关键词: fifth-generation new radio (5G-NR), high-speed railway (HSR), deep learning (DL), back propagation neural network (BPNN), Doppler frequency offset (DFO) estimation |
| 基金项目: |
| Author Name | Affiliation | | YANG Lihua(杨丽花)* | (*College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, P.R.China)
(**College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R.China) | | WANG Zenghao* | (*College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, P.R.China)
(**College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R.China) | | ZHANG Jie* | (*College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, P.R.China)
(**College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R.China) | | JIANG Ting** | (*College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, P.R.China)
(**College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| In the fifth-generation new radio (5G-NR) high-speed railway (HSR) downlink, a deep learning (DL) based Doppler frequency offset (DFO) estimation scheme is proposed by using the back propagation neural network (BPNN). The proposed method mainly includes pre-training, training, and estimation phases, where the pre-training and training belong to the off-line stage, and the estimation is the online stage. To reduce the performance loss caused by the random initialization, the pre-training method is employed to acquire a desirable initialization, which is used as the initial parameters of the training phase. Moreover, the initial DFO estimation is used as input along with the received pilots to further improve the estimation accuracy. Different from the training phase, the initial DFO estimation in pre-training phase is obtained by the data and pilot symbols. Simulation results show that the mean squared error (MSE) performance of the proposed method is better than those of the available algorithms, and it has acceptable computational complexity. |
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