## 1 Introduction  
5G and beyond communication systems are revolutionizing communication in today's rapidly evolving technological landscape. These systems provide a significant increase in access to Internet-based services with high speeds, low latency, and wide bandwidth. They offer users a seamless experience across multiple devices, facilitating integration between mobile devices, desktops, and other platforms. They also support innovative features and applications, enabling technologies such as augmented reality, remote interventions, and the Internet of things. Owing to their flexibility and future-proof adaptability, these systems play a key role in digital transformation, bringing a more efficient, secure, and rich experience to the world of communications. However, all of these features also open up the possibility for malicious jammers to attack more targets and corrupt more data. Therefore, fast, accurate, and effective detection of jamming attacks is vital for increasing the defense capabilities of systems. Several jamming detection methods are proposed for wireless networks [1, 2]. A significant number of these utilize received signal strength (RSS) measurements. The authors of [3, 4, 5, 6, 7] obtain the RSS by estimating the spectrum of the received signal and observe the effect of jamming signals on the RSS. In other studies, the optimal RSS thresholds for jamming detection are determined with likelihood tests performed by considering the jamming presence and absence hypotheses. This method is widely proposed for massive SIMO [8], massive MIMO [9], LTE [10], direct-sequence spread-spectrum (DSSS) [11], wireless sensor networks [12], ad hoc networks [13], cognitive radio networks [14] and satellite communication [15] systems. In addition, many studies propose the use of RSS-based metrics in combination with bit error rate (BER)-based metrics such as throughput, packet error rate, packet delivery ratio, packet sent ratio, packet loss rate, and bad packet ratio. Accordingly, the effects of jamming are observed jointly on the RSS- and BER-based metrics, and jamming detection threshold levels are set on these metrics. The jamming detection performance of such methods is demonstrated with simulations in [16; 17; 18; 19; 20], and with experimental studies as well as simulations in [21; 22; 23]. On the other hand, in [24; 25; 26; 27], these metrics are used to train machine learning algorithms such as support vector machines, neural networks, and random forests for jamming detection. In addition to the aforementioned metrics, the chip error rate [28] and inter-arrival time [29] are other metrics examined for jamming detection. Machine learning algorithms are also trained using spectrogram images [30], IQ samples [31], time-domain signal samples [32], and FFT samples [33; 34] for jamming detection. Although machine learning algorithms are becoming increasingly popular, the issues of training these algorithms, collecting sufficient data for training, adapting to varying jamming strategies, and integrating them into the system architecture with minimal overhead must be considered. Subspace analysis methods are the other methods used in jamming detection. Such methods use eigenvalue [35] or singular-value [36] analyses to identify the subspaces formed by the signal and jamming. However, the jamming detection success of such methods requires the jamming level to be sufficiently higher than the legitimate signal level. In our previous study [37], the EVM vs. RB metric was proposed to detect jamming attacks in 5G networks. The error vector magnitude (EVM) is measured for each resource block (RB) in the received signal and jamming signals are then detected at RBs where the EVM upper threshold is not met. The success of EVM vs. RB in terms of sensitivity compared to classical BER-based methods was verified with simulations containing only a limited number of scenarios. The EVM metric is also used in studies [38; 39] to study jamming effects in OFDM systems.