CWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets
This addresses security threats from jamming attacks in 5G-NR wireless networks, but it is incremental as it builds on existing methods like CAE and CWGAN-GP for data augmentation.
The paper tackled jamming detection in 5G-NR networks using a Convolutional Autoencoder (CAE) on non-IID datasets, achieving average performance metrics of 97.33% precision, 91.33% recall, 94.08% F1-score, and 94.35% accuracy, outperforming benchmark models.
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.