Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Model

Implementation of the paper, "Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Model", published in IEEE Transactions on Signal Processing. A shorter, conference version of this paper is published in IEEE ICASSP 2021, available here.

Spectrum Cartography (SC) Problem:

[Left] SC environment. [Right] SC task.

Proposed Method:

Radio map model.

Method 1: Nonnegative matrix factorization ASsisted Deep emitter spAtial loss Completion (Nasdac) .

Method 2: Deep generative priOr With Joint OptimizatioN for Spectrum cartography (Dowjons).

Results:

Completed Radio maps using different methods.

Recovered PSD by the proposed methods.

Recovered SLF by the proposed methods.

Installation:

The code was built with the python3.6, matlab2020b, and torch=1.10.2

To run the code follow the followign installation instructions:

1. Install all python packages located in requirements.txt.

2. Download tensorlab from  https://www.tensorlab.net

3. Make sure that the above packages are in your environment path.

4. To interface the python models from matlab use instructions provided here: https://www.mathworks.com/help/matlab/matlab_external/create-object-from-python-class.html.

Usage:

Sample demonstration of the proposed method in the paper is available in experiments/demo.m.

Training Deep Prior:

To train a deep prior model follow the following steps:

• Go to deep_prior/generate_data and in the generate_slf.m, provide destination paths for the training data. Also specify other parameters that suit your need. Then run the script.

• Convert and save the generated matlab tensors as pytorch tensor for faster data loading during training by running the following command from the base directory:

  cd deep_prior
  python convert_to_torch_tensor.py --data_folder <path to the training data> --save_folder <path to save the converted data>

• Train model by running the following from the base directory

  cd deep_prior
  python train.py --train_data_folder <path to the training data> --validation_data_folder <path to validation data> --model_path <path to save the model> --img_size <length of your radio map region [square region is assumed]>

The model will be saved in the path provided in --model_path.