SPIRE Self-suPervIsed SpectRum ClusteRing

Code for the eperimental work as part of the paper: Spectrum Sensing with Deep Clustering: Label-Free Radio Access Technology Recognition.

Given the dynamic and complex nature of contemporary wireless environments, characterized by a wide variety of devices and multiple RATs, spectrum sensing is envisioned to become a building component of future 6G, including as a components within O-RAN or digital twins.

In this work, we propose a new spectrum sensing workflow in which the model training does not require any prior knowledge of the RATs transmitting in that area (i.e. no labelled data) and the class assignment can be easily done through manual mapping. Furthermore, we adaptat a SSL deep clustering architecture capable of autonomously extracting spectrum features from raw 1D Fast Fourier Transform (FFT) data. We evaluate the proposed architecture on three real-world datasets from three European cities, in the 868 MHz, 2.4 GHz and 5.9 GHz bands containing over 10 RATs and show that the developed model achieves superior performance by up to 35 percentage points with 22% fewer trainable parameters and 50% less floating-point operations per second (FLOPS) compared to an SotA AE-based reference architecture.

Self-supervised clustering-enabled spectrum sensing workflow

To run the expreiments:

1. Install dependencies:

   pip install -r requriments.txt
   pip install -q pyclustertend==1.8.2 --ignore-requires-python --no-deps

2. Download the datasets to corresponding folders in data/

   data/ITS: https://ieee-dataport.org/documents/dataset-iq-samples-lte-5g-nr-wi-fi-its-g5-and-c-v2x-pc5
   data/TCD: https://github.com/JaronFontaine/Technology-Recognition-dataset-of-real-life-LTE-WiFi-and-DVB-T
   data/LOG-a-TEC: https://log-a-tec.eu/

3. Do preproccesing of data:

   data/ITS: python preprocess_data_its.py <dir_of_data>/ <your_dataset_name>.h5
   data/TCD: python preprocess_data_ghent.py <dir_of_data>/ <your_dataset_name>.h5
   data/TCD: python preprocess_data_logatec.py <filepath>.json <your_dataset_name>.h5

4. Run the notebooks