With the evolution of 5G wireless communications, the Synchronization Signal
Block (SSB) plays a critical role in the synchronization of devices and
accessibility of services. However, due to the predictable nature of SSB
transmission, including the Primary and Secondary Synchronization Signals (PSS
and SSS), jamming attacks are critical threats. By leveraging RF domain
knowledge, this work presents a novel deep learning-based technique for
detecting jammers in 5G networks. Unlike the existing jamming detection
algorithms that mostly rely on network parameters, we introduce a double
threshold deep learning jamming detector by focusing on the SSB. The detection
method is focused on RF domain features and improves the robustness of the
network without requiring integration with the pre-existing network
infrastructure. By integrating a preprocessing block that extracts PSS
correlation and energy per null resource elements (EPNRE) characteristics, our
method distinguishes between normal and jammed received signals with high
precision. Additionally, by incorporation of Discrete Wavelet Transform (DWT),
the efficacy of training and detection are optimized. A double threshold double
Deep Neural Network (DT-DDNN) is also introduced to the architecture
complemented by a deep cascade learning model to increase the sensitivity of
the model to variations of signal to jamming noise ratio (SJNR). Results show
that the proposed method achieves 96.4% detection rate in extra low jamming
power, i.e., SJNR between 15 to 30 dB which outperforms the single threshold
DNN design with 86.0% detection rate and unprocessed IQ sample DNN design with
83.2% detection rate. Ultimately, performance of DT-DDNN is validated through
the analysis of real 5G signals obtained from a practical testbed,
demonstrating a strong alignment with the simulation results.

使用深度学习和 RF 领域特征提取的双阈值检测方法，通过提取 PSS 相关性和 EPNRE 特征，用于区分正常和受干扰的 5G 信号，进一步优化了训练和检测效果，并通过实际测试结果验证了 DT-DDNN 的性能。