An Efficient EVM Based Jamming Detection in 5G Networks

5G is expected to support high data rate, low latency and massive number of connectivity requirements. Due to these features, 5G will serve many different tasks such as increased reality, internet of things, emergencies and remote interventions, besides civil communication. Therefore, it has become more important to detect malicious jamming attacks in 5G. Signal level and error rate metrics have been widely proposed for jamming detection in various wireless networks. However, most of the metrics are insufficient to detect jamming at high SJR (Signal-to-Jamming Ratio) values. On the other hand, for 5G networks, we propose a more sensitive and informative jamming detection method that exploits the "EVM-vs-RB"measurement obtained by calculating EVM (Error Vector Magnitude) metric for each RB (Resource Block). The presence of jamming is then detected when the predetermined threshold level for EVM is exceeded on the proposed measurement set. As the EVM information is obtained for each RB, it is also revealed which RBs, that is frequency bands, are subject to the jamming attack. Obtained simulation results show that jamming attacks, which cannot be detected in high SJR situations with the classical metrics, can be detected by the proposed method even at high SJR values such as 20 dB.