Jamming attacks pose a significant threat to 5G networks and connected systems such as vehicular networks, critical infrastructure, and public safety communications. This paper presents an extensive survey of jammer types, detection algorithms, mitigation strategies, and localization techniques. In addition to the survey, we present a unified framework to detect and localize Gaussian noise jammers (and extendable to smart/protocol-aware jammers) by combining fourth-order cumulant-based 2D-MUSIC for angle-of-arrival estimation with unsupervised anomaly detection via adversarial autoencoders. We derive the relevant higher-order statistical relations, explain the 2D-MUSIC extension using cumulant matrices, describe the adversarial autoencoders architecture and training loss used for anomaly detection, and propose fusion schemes to produce robust geolocation estimates. Simulation parameters and scenario configurations are drawn from our experimental slides and implementations.
Kulhandjian et al. (Sun,) studied this question.