Telecommunications Science ›› 2023, Vol. 39 ›› Issue (4): 71-86.doi: 10.11959/j.issn.1000-0801.2023097
• Research and Development • Previous Articles Next Articles
Min LU1, Zehao QIN1,2, Zhihui CHEN1,2, Min ZHANG1,2, Guangxue YUE2,3
Revised:
2023-04-12
Online:
2023-04-20
Published:
2023-04-01
Supported by:
CLC Number:
Min LU, Zehao QIN, Zhihui CHEN, Min ZHANG, Guangxue YUE. 1D-Concatenate based channel estimation DNN model optimization method[J]. Telecommunications Science, 2023, 39(4): 71-86.
"
网络层名称 | 网络类型 | 输出大小 |
Input | Feature Input | (54,1) |
FC 1 | Fully Connected | Size of baseline model |
FC 2 | Fully Connected | Size of baseline model |
… | … | … |
… | … | … |
… | … | … |
FC L-1 | Fully Connected | Size of baseline model |
1D-Concat | Concat[FC 2, FC L-1] | (Size of(FC 2+FC L-1),1) |
FC L | Fully Connected | (108,1) |
Output | Regression Output | Regression |
"
模型名称 | 数据名称 | 数值 | ||||||
DenseSimNet | 初始学习率 | 0.010 | 0.015 | 0.020 | 0.025 | 0.030 | 0.035 | 0.040 |
平均MSE×102 | 1.61 | 1.51 | 1.48 | 1.46 | 1.45 | 1.45 | 1.45 | |
DenseFC-DNN | 初始学习率 | 0.030 | 0.035 | 0.040 | 0.045 | 0.050 | 0.055 | 0.060 |
平均MSE×102 | 2.48 | 1.98 | 1.73 | 1.74 | 2.14 | 1.79 | 1.78 | |
DenseDL-based | 初始学习率 | 0.010 | 0.015 | 0.020 | 0.025 | 0.030 | 0.035 | 0.040 |
平均MSE×102 | 2.33 | 2.03 | 1.93 | 1.82 | 1.86 | 1.67 | 1.68 | |
DenseLS_RefineNet | 初始学习率 | 0.010 | 0.015 | 0.020 | 0.025 | 0.030 | 0.035 | 0.040 |
平均MSE×102 | 1.53 | 1.46 | 1.45 | 1.44 | 1.44 | 1.44 | 1.44 | |
DenseDNN-1 | 初始学习率 | 0.005 | 0.010 | 0.015 | 0.020 | 0.025 | 0.030 | 0.035 |
平均MSE×102 | 3.30 | 3.00 | 3.11 | 3.09 | 2.67 | 2.87 | 2.69 | |
DenseDNN-2 | 初始学习率 | 0.005 | 0.010 | 0.015 | 0.020 | 0.025 | 0.030 | 0.035 |
平均MSE×102 | 2.41 | 1.55 | 1.48 | 1.43 | 1.38 | 1.42 | 1.42 |
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