In order to generates datasets required for training and testing DyLoc, you have to start with matlab codes. First of all, you should generate scattring environments using DeepMIMO. The codes for DeepMIMO and instruction for using it can be found in DeepMIMO. After you create your environment based on the TABLE I of the paper (or any other arbitrary parameters you choose), you should store the resulted struct array in "DeepMIMO_dataset". Next go the "Matlab Codes" folder. "Dataset_gen.m" generates a dataset (pairs of CSI-Location) for training DCNN, and "Dataset_gen_FPL.m" generates 1000 testframes for evaluating Dyloc. "Moving_Dataset_gen.m" generates the moving datasets for training and testing PredRNN. All of these three codes save their output in CSV files.

Next, go to "mat2npz" folder, copy the output CSVs of Matlab codes in the "Data" folder and then run "run.py" code to convert CSVs to one consolidated npz file.

Next, go to DCNN training folder, copy the output npz files to "Data" folder and run "run.py". The output weights will be stored in "weights" folder.

Next, go to KNNCNN folder. First copy the output datasets (npz files) to "Data" folder. Then run "MakeADPgrid.py" to form and save the grid in "ADP" folder. Then run "train.py" to train the classification DCNN. The weights will be saved in "KNNCNNW1" folder. Finally, you can test the performance of DCNN+WKNN in the three mentioned scenarios in the paper by running "integration.py". The RSE (root square error) of each experiment will saved in "Results" folder.

Next, go to PredRNN training folder, copy the output npz files to "Data" folder and run "run.py". The output weights will be stored in "checkpoints/mnist_predrnn" folder.

Finally, everything is available to test DyLoc. Copy PredRNN weights to "PredRNN Models" folder. Copy DCNN weights to "DCNNweights" folder. Copy npz files to "Data" folder. Next run "run.py". The code will measure the performance of DyLoc and DCNN in the three mentioned scenarios in the paper. The RSE of each experiment will saved in "Results" folder.

(for Farzam: change the name of CSV that containes results in the code)

In case you dont want to bother yourself running matlab codes. All reqired .npz files can be found in Datasets. You need to copy and paste them in Data folder for doing the correspong job! Having this foler, there is no need for running any Matlab codes or conversion from CSV to NPZ.

The Datasets folder that is available on google drive contains:

• TrainDCNN1.npz (I3) and DCNNtrain.npz (O1): Containing all possible CSI-Location pairs all over the environments
• TestframesI2.npz (I3) and TestframesO1.npz (O1): Containing 1000 time series of 20 frames for all three dynamic scenarios used for testing DyLoc
• moving-ADP-train-O1.npz (I3) and moving-ADP-train-I3.npz (O1): Containing 10000 time series of 20 frames training PredRNN

If you dont want to bother training and testing networks, we left weights files that can be used by the networks, so just download the "Data" folder and run the "run.py" and enjoy!

To improve the results significantly specifically for both O1 and I3 environments, you only need to increase bandwdith or the number ULA elements.

If you have any question regarding the codes, dont hesitate to contact me at [email protected].