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This code is for the following paper: H. He, C. Wen, S. Jin, and G. Y. Li, “Deep learning-based channel estimation for beamspace mmwave massive MIMO systems,” IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 852–855, Oct. 2018.
This code is for the following paper:
H. He, C. Wen, S. Jin, and G. Y. Li, “Deep learning-based channel estimation for beamspace mmwave massive MIMO systems,” IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 852–855, Oct. 2018.
Please cite the paper when use this code.
Install Matcovnet
Choose the gpu version and verify you have a supported GPU and that the latest driver is installed.
Run main program
Location: ..\LDAMP_for_Rice\D-AMP_Toolbox-master\SCAMPI-MATLAB\scampi-vs-ssd\
main_new_old.m: main function, run and call trained network and denoiser to produce simulation results
test.m: Generate the Saleh-Valenzuela channel model
Trained network is saved at ..\Code_WCL_2018\LDAMP_for_Rice\D-AMP_Toolbox-master\Packages\DnCNN\BestNets_20
Location: ..\LDAMP_for_Rice\D-AMP_Toolbox-master\Packages\DnCNN\Training
Demo_Train.m: main function for training the DnCNN
Demo_test: main fuction for test the DnCNN
Training_h.mat: Training data
Vali_h.mat: Validata and Test data
rescaleImage.m: rescale the channel into [0,1]
channel_gen.m: generate the training and test data
Many trained network will be saved at ..\LDAMP_for_Rice\D-AMP_Toolbox-master\Packages\DnCNN\Training\NewNetworks. Copy the best network with different SNR and rename to ..\LDAMP_for_Rice\D-AMP_Toolbox-master\Packages\DnCNN\BestNets_20.
Location: ..\LDAMP_for_Rice\D-AMP_Toolbox-master\SCAMPI-MATLAB and ..\LDAMP_for_Rice\D-AMP_Toolbox-master\Algorithms
DAMP_SNR1.m: D-AMP algorithm
Acknoledge:
Many thanks for Dr.Christopher A. Metzler share the code selflessly. Instructions about installing the matcovnet and using LDAMP network can refer to
website: https://github.com/ricedsp/D-AMP_Toolbox
Questions/suggestions/comments about LDAMP-based Channel estimation network: hehengtao@seu.edu.cn
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