A robust framework covering measures developed using EVM metric against jamming attacks in next-generation communication systems

Abstract

In the face of escalating threats posed by malicious jamming in next-generation communication systems, this thesis presents a comprehensive framework comprising jamming signal detection, jamming signal classification, jammer localization, and two anti-jamming strategies to address these challenges effectively. The proposed error vector magnitude vs. resource block (EVM vs. RB) methodology for jamming signal detection, unlike traditional approaches commonly use received signal strength (RSS) and bit error rate (BER), examines the effects of jamming signals on IQ symbols using the EVM metric. Our methodology, which is able to capture even small changes imposed by jamming signals on IQ symbols, provides significant advantages in terms of sensitivity compared to the conventional approaches. Moreover, the proposed methodology has a low-complexity of the order of O(N), which is especially important for next-generation communication systems known for their strict latency requirements. By utilizing IQ symbols that are natively generated in the data transmission system, our methodology seamlessly integrates into existing 5G and LTE systems without imposing additional overhead, facilitating practical deployment in real-world scenarios. RBs represent the frequency domain for next-generation wireless communication systems and the proposed methodology measures the EVM for each RB in the received signal. In this way, our approach not only detects jamming signals but also provides vital frequency information associated with the jammer. This information enhances counteraction capabilities, enabling targeted responses to mitigate the impact of jamming attacks. Furthermore, the proposed method demonstrates stability across varying system parameters, including modulation type and code rate, thereby contributing to adaptability in dynamic communication environments. The effectiveness of the proposed detection methodology is validated through extensive theoretical analysis, simulation studies, and laboratory experiments. Theoretical analyses substantiate the advantages of our approach, reinforcing its validity and reliability. Simulation results showcase the robustness and stability of our method across diverse scenarios, highlighting its practical utility in real-world applications. Laboratory experiments provide empirical evidence of its effectiveness, further validating its potential for deployment in operational communication systems. Beyond the jamming signal detection, our jamming signal classification methodology offers a comprehensive solution to accurately characterize and classify various jamming signals by utilizing Symbol-RB-EVM which is another measurement we developed. The Symbol-RB-EVM measurement is created by accumulating the EVM vs. RB data obtained for each OFDM symbol time into a matrix and provides a nuanced understanding of jamming signal behavior across time and frequency domains. Unlike traditional measurements such as spectrogram, RSS, and BER, the proposed measurement offers superior sensitivity and specificity in capturing the intricacies of jamming signals. After creating the dataset using the Symbol-RB-EVM results, we take advantage of machine learning algorithms for jamming signal classification. Thanks to the fact that Symbol-RB-EVM efficiently provides useful features of jamming signals, the proposed methodology enables precise classification of jamming types with high accuracy, thereby minimizing false alarms. This property of Symbol-RB-EVM also enables lower complexity machine learning methods to produce successful results even with minimal training data. Add to this the fact that Symbol-RB-EVM is computed with low computational complexity (O(N)), and we can say that the proposed methodology is in a very valuable position in terms of overall complexity. Extensive simulations demonstrate the superior performance of the proposed metodology in accurately characterizing diverse jamming signal types across varying scenarios and environmental conditions. In addition to the detection and classification, the EVM metric is also considered to provide effective results for jammer localization problem. The localization process begins with the detection of jamming signals using the EVM vs. RB methodology. EVM contours are then drawn on the map using the EVM data acquired from user equipments (UEs). In this approach, which has a range-free feature, the contours are concentrated towards the jammer source, providing a coarse estimate of the location of the jammer. WhentheUEdensitysurroundingthejammerissufficiently high, accurate localization can be swiftly achieved using these contours alone, eliminating the need for further operations. However, in cases where the UE density is not sufficient for an accurate localization, we take our methodology to a hybrid structure by also using Time Difference of Arrival (TDOA), a range-based technique, to improve localization accuracy. With the help of the coarse location information from the EVM contours, the right one is selected quickly among the sensitive solutions offered by the TDOA. Thanks to such an innovative approach, the quickness of the range-free technique and the high accuracy of the range-based technique are combined. Extensive simulations demonstrate the localization success of the proposed methodol ogy across diverse network densities and environmental conditions, underscoring its robustness and reliability in real-world deployment scenarios. By offering both high accuracy and low complexity, our methodology promises to bolster the resilience of 5G networks against malicious jamming attacks, ensuring uninterrupted communication services and safeguarding critical data transmission pathways. Effective solutions presented in the fields of jamming signal detection, classification and jammer localization encourage us to develop successful anti-jamming solutions. The first anti-jamming proposal provides a robust method designed to protect next-generation communication systems from malicious jamming. At the beginning of this methodology is the identification of RBs affected by jamming attacks through the EVM vs. RB measurement. By leveraging this measurement, which provides insight into the frequency domains targeted by jammers, our methodology effectively discerns clean RBs from those under jamming influence. Building upon this insight, we propose an RB sharing strategy aimed at optimizing resource allocation and protecting UE from jammer interference. The strategy prioritizes the allocation of clean RBs to UEs closest to the jammer, thereby isolating their signals from jamming attacks and ensuring uninterrupted communication. Acknowledging the finite nature of RB resources, our research endeavors to assess jammed RBs and allocate them to UEs farthest from the jammer whenever possible. Alternatively, we are also investigating data rate reduction strategies that can be realized for these RBs in order to increase their resistance to jamming. Key to the success of our methodology is its low-complexity decision-making process, which eliminates the need for extensive training and ensures rapid response capabilities—critical attributes in the context of next-generation communication systems characterized by low-latency requirements. Moreover, our approach seamlessly integrates with existing system architectures, leveraging IQ data obtained from the inherent system flow for necessary EVM measurements. Simulation results underscore the efficacy of the proposed methodology in maintaining maximum UE throughput, even in the face of sustained jamming attacks. By optimizing RB resource utilization and minimizing disruptions caused by jamming interference, our approach promises to bolster the resilience of next-generation communication systems against evolving threats and ensuring uninterrupted service delivery. Our second anti-jamming proposal introduces a novel methodology engineered to confront the challenges posed by malicious jamming attacks head-on. At its core lies a sophisticated approach that harnesses transmitted and received IQ symbols to train a linear regression algorithm, enabling the system to adapt and neutralize the disruptive effects of jamming signals on the IQ symbol packets. Utilization of the EVM metric gauges the training performance of the linear regression algorithm. Through an iterative process, the algorithm assimilates the impact of jamming signals on IQ symbols, effectively deciphering their disruptive effects and restoring communications for jammer-occupied resources. One of the key strengths of our methodology lies in its adaptability to diverse jamming signals, ensuring robust protection against a wide range of jamming tactics. By efficiently restoring communications for jammer-occupied resources, our approach minimizes the impact of jamming attacks on network performance, ensuring uninterrupted service delivery for end-users. Moreover, the low-complexity implementation is facilitated by leveraging linear regression and EVM techniques. Theoretical analyses and simulation results confirm the effectiveness of the proposed methodology and underline its potential to increase the resilience of communication infrastructures against malicious interference.