Channel-Estimation-and-Hybrid-Precoding-for-Millimeter-Wave-Systems-Based-on-Deep-Learning

1. Put the "matlab" and "python" folders in a root directory

2. The "matlab" folder contains traditional HBF algorithm, channel estimation algorithm and data generation code. The "python" folder contains the defined neural network models and the trained models.

3. If you want to test the HBF-Net and CE-HBF-Net directly, you can

• Run "matlab/channel_gen.m" to generate test channel. Run "matlab/gen_testdata.m" to generate test dataset
• You can also click here (Extraction code: om9r) to download the data set without generating a new test data set.
• The trained models are saved in "python/model". Run "python/main.py" in test mode (train_flag=0), you can test the performance of HBF-Net and CE-HBF-Net.

4. If you want to retrain the HBF-Net and CE-HBF-Net, you can

• Run "matlab/gen_traindata.m" to generate training data set
• Set "python/main.py" to training mode (train_flag=1), you can retrain the corresponding neural network model

Pay attention to the correspondence between the saved data path and the load data path