Exploring Deep Learning Architectures for RF Signal Classification

Exploring Deep Learning Architectures for RF Signal Classification

Paper in proceedings (conference paper)

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.

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.

Classification; Channel models; Dataset; Deep learning; Neural networks; RF impairments; RF signals

Classification; Channel models; Dataset; Deep learning; Neural networks; RF impairments; RF signals

POLÁK, L.; TURÁK, S.; ŠOTNER, R.; KUFA, J.; MARŠÁLEK, R.; DHAKA, A.

12.05.2025

IEEE

Brno

979-8-3315-4447-8

35th International Conference Radioelektronika

1

1

6

6

https://ieeexplore.ieee.org/document/11008396

http://hdl.handle.net/11012/255541

Radioelektronika_2025_RF_DL
RADIOELEKTRONIKA65656.2025.11008396_accepted