Jamming Detection

Jamming detection focuses on identifying and mitigating intentional interference in wireless communication systems, safeguarding the reliability and security of various applications. Current research heavily utilizes machine learning, particularly deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), along with other techniques such as support vector machines and Bayesian networks, to classify jamming signals based on features extracted from the radio frequency (RF) domain or protocol stacks. This field is crucial for ensuring the robustness of 5G networks, GNSS systems, and UAV communications, with advancements directly impacting the security and performance of these critical technologies.