1D-Concatenate based channel estimation DNN model optimization method
In order to improve the channel estimation accuracy of DNN model in wireless communication, a DNN model optimization method based on 1D-Concatenate was proposed.In this method, Concatenate performs one-dimensional data transformation, the DNN model was introduced by hopping connection, the gradient...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2023-04-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023097/ |
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| Summary: | In order to improve the channel estimation accuracy of DNN model in wireless communication, a DNN model optimization method based on 1D-Concatenate was proposed.In this method, Concatenate performs one-dimensional data transformation, the DNN model was introduced by hopping connection, the gradient disappearance problem was suppressed, and 1D-Concatenate was used to restore the data features lost during network training to improve the accuracy of DNN channel estimation.In order to verify the effectiveness of the optimization method, a typical DNN-based wireless communication channel estimation model was selected for comparative simulation experiments.Experimental results show that the estimated gain of the existing DNN model can be increased by 77.10% by the proposed optimization method, and the channel gain can be increased by up to 3 dB under high signal-to-noise ratio.This optimization method can effectively improve the channel estimation accuracy of DNN model in wireless communication, especially the improvement effect is significant under high signal-to-noise ratio. |
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| ISSN: | 1000-0801 |