CWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets

TL;DR

This paper introduces a novel CWGAN-GP augmented CAE approach for detecting jamming in 5G-NR networks using non-IID datasets, achieving high accuracy and robustness against data heterogeneity.

Contribution

The study proposes a new CWGAN-GP based data augmentation method combined with a CAE for improved jamming detection in complex 5G datasets.

Findings

Achieved 97.33% precision and 94.35% accuracy in jamming detection.

Demonstrated robustness of CAE across heterogeneous datasets.

Outperformed benchmark autoencoders in detection performance.

Abstract

In the ever-expanding domain of 5G-NR wireless cellular networks, over-the-air jamming attacks are prevalent as security attacks, compromising the quality of the received signal. We simulate a jamming environment by incorporating additive white Gaussian noise (AWGN) into the real-world In-phase and Quadrature (I/Q) OFDM datasets. A Convolutional Autoencoder (CAE) is exploited to implement a jamming detection over various characteristics such as heterogenous I/Q datasets; extracting relevant information on Synchronization Signal Blocks (SSBs), and fewer SSB observations with notable class imbalance. Given the characteristics of datasets, balanced datasets are acquired by employing a Conv1D conditional Wasserstein Generative Adversarial Network-Gradient Penalty(CWGAN-GP) on both majority and minority SSB observations. Additionally, we compare the performance and detection ability of the proposed CAE model on augmented datasets with benchmark models: Convolutional Denoising Autoencoder (CDAE) and Convolutional Sparse Autoencoder (CSAE). Despite the complexity of data heterogeneity involved across all datasets, CAE depicts the robustness in detection performance of jammed signal by achieving average values of 97.33% precision, 91.33% recall, 94.08% F1-score, and 94.35% accuracy over CDAE and CSAE.