## 1 Introduction
However, these studies have aimed to identify the jamming strategies that cause the greatest damage to the system. In this paper, we extend the work for the EVM vs. RB measurement and list below all the innovations and contributions achieved:  
1. EVM metric utilization: The paper advocates for the utilization of the EVM metric measured in IQ symbols, a departure from the commonly used classical RSS and BER based metrics in the literature. 2. Enhanced sensitivity: The proposed method demonstrates a significant improvement in jamming detection sensitivity compared to existing approaches. Although low-power hidden jamming signals that cannot be detected using conventional metrics do not cause denial of service, they can limit the data transmission rate. Due to the EVM's ability to detect small variations in jamming level, jamming signals hidden in an extreme form 20 dB below the legal signal are also successfully detected. 3. Low complexity: For next-generation networks with low latency requirements, it is advantageous that the proposed method has a low complexity of \(O(N)\). This advantage also contributes to the fast response of the system for anti-jamming measures. 4. Jammer frequency information: The proposed method calculates the EVM metric for each RB in the received signal. Since RBs represent the frequency domain, the frequency bands in which jamming attacks occur are also revealed. This important information, which is not provided by most methods, offers an important background for countermeasure steps such as jammer localization [40] and antijamming frequency planning. In addition, the concepts of ambient backscattering and RF energy harvesting [41; 42] are recently proposed as solutions to the battery problems of IoT devices. By using the jamming frequency information provided by our method, these devices can be tuned to the correct jamming frequencies and, as a result, jamming energy, which is usually emitted at high RF powers, can be utilized. 5. Reliability: The EVM vs. RB measurement provides a stable jamming detection performance against varying system parameters such as modulation degree and code rate. However, BER-based methods are affected by the variations of these parameters and provide unreliable results. 6. Usability and compatibility: In LTE and 5G systems, it is known that reference IQ symbols are also sent in the transmitted data packet to enable the UE to estimate the channel. The EVM metric used by the proposed method is calculated using these reference symbols that are already in the system architecture. Thus, the proposed method can be easily integrated into the system without the need for changes in system operation or hardware. Moreover, since jamming detection can be performed using a single threshold level for the EVM metric, there is no need for any pre-operational training and validation phases. As a result, the proposed method is suitable for LTE, 5G, and beyond communication systems, which include IQ modulation and resource block (RB) architectures. 7. Theoretical analysis support: All presented advantages are substantiated with thorough theoretical analysis, reinforcing the validity and efficacy of the proposed jamming detection methodology. 8. Versatility in system scenarios: The proposed method's successful operation in different system scenarios is underscored by extending the simulation conditions to cover the sub-6 GHz frequency region usage, different numerology (OFDM subcarrier spacing) usage, line-of-sight (LOS) and non-line-of-sight (NLOS) channel cases, MIMO structures, and millimeter-wave (mmWave) band usage scenarios. 9. Laboratory experiment validation: The study's success is conclusively demonstrated through experiments conducted in a laboratory environment, providing empirical evidence of the method's effectiveness.