Source code for Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces, modified from DnCNN.
S. Liu, Z. Gao, J. Zhang, M. D. Renzo and M. -S. Alouini, "Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces," in IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 9223-9228, Aug. 2020, doi: 10.1109/TVT.2020.3005402.
To successfully run the code, you should follow the steps below
-
Make sure you have installed the required libs
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Generate channel dataset. We have used the geometric channel model in our paper with vary parameters.
Note that the required shape is
- Set the parameters in
modelTrain.py
and create the corresponding folders before running the program. - If you want to evalutate the performance, please change the dataset configurations in
modelTrain.py
文章 Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces 的源代码, 由 DnCNN 等修改。
为保证成功运行,您应按照以下步骤进行
- 确保您已安装所有需要的库
- 产生一组信道数据集。此处我们采用几何信道建模,参数在仿真中有变。
值得注意的是,信道数据集的尺寸要求为$[N,2,N_{IRS},N_{C}]$,其中2代表信道矩阵的实部和虚部。
- 运行
modelTrain.py
进行训练,在这之前要设置合适的参数(用于区别保存模型时的路径)。 - 若需要测试性能表现,请将训练文件中的数据集配置换成测试集
原文采用OMP算法构造数据集,该部分代码可能在后续整理中发布。