Exploring Deep Learning Architectures for RF Signal Classification
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Date
2025-05-12
Authors
Polák, Ladislav
Turák, Samuel
Šotner, Roman
Kufa, Jan
Maršálek, Roman
Dhaka, Arvind
0000-0001-7084-6210Advisor
Referee
Mark
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Publisher
IEEE
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Abstract
Future 6G radio networks will heavily rely on deep learning (DL) models for both signal and data processing. DL-based solutions can be highly effective in classifying various radio frequency (RF) signals influenced by noise or intentional jamming as they are capable of recognizing patterns even under challenging conditions. This paper focuses on the classification of different RF signals using three DL-based models: CNN, GRU, and CGDNN. For this purpose, a dataset containing RF signals influenced by various impairments (e.g., I/Q-imbalance) and transmission conditions (e.g., multipath propagation) was created using MATLAB. Both the dataset and the source code have been made publicly available to support further research in this area. Preliminary results shown that the performance of DL-based approaches depends not only on the RF impairments considered but also on the preparation of the dataset.
Description
Citation
35th International Conference Radioelektronika. 2025, p. 1-6.
https://ieeexplore.ieee.org/document/11008396
https://ieeexplore.ieee.org/document/11008396
Document type
Peer-reviewed
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Accepted version
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Language of document
en
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Defence
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(C) IEEE