Presence Of Jamming Research Articles

Clustered jamming in U-V2X communications with 3D antenna beam-width fluctuations

Jamming is the disruption of wireless communication signals, rendering them unreadable or degraded, typically caused by jamming devices. Clustered jamming in vehicular communications is a highly sophisticated attack technique characterized by the coordinated deployment of multiple jammers with the strategic intent to deliberately disrupt and incapacitate communication links, thereby presenting a formidable challenge to the reliability and security of vehicular networks. It is critical to address the intricate challenges imposed by clustered jamming in the context of vehicle-to-everything (V2X) communications augmented by unmanned aerial vehicles (UAVs), given their critical role in facilitating essential vehicle-to-vehicle and vehicle-to-UAV communication links. In this paper, we examine UAV-V2X (U-V2X) communications within the context of clustered jamming and the variations in the 3D beam-width of antennas. Also, mitigation techniques for clustered jamming in V2X communications are addressed. We consider the distribution of UAVs is governed by the Poisson point process, the distribution of vehicular-nodes (VNs) is governed by the Poisson line process, and the distribution of jammers is governed by the Matern Cluster Process. The analytical equations for the association probability and the success probability (SP) of a typical VN with the UAV and near-by VN, along with the presence of jammers and the variations of antenna beam-width are provided. Furthermore, we investigate the SP and spectral efficiency performance of the U-V2X network under different network configurations of UAVs, VNs, roads, jamming clusters, and transmit power of jamming devices. Our findings reveal that clustered jammers influence U-V2X network’s performance, further compounded by UAV beam-width fluctuations. Moreover, in low-density areas, UAVs demonstrate superior reliability for connectivity compared to VNs, despite clustered jamming. Also, lower UAV antenna fluctuations enhance UAVs’ dependability in U-V2X networks amidst clustered jamming. Specifically, for a given setup with τ= -5 dB, U-V2X link’s SP with jamming drops to one-quarter due to 2-degree beam-width fluctuations. Consequently, it is imperative to design and implement anti-jamming strategies, particularly as the number of jamming clusters and variations of antenna beam-width increase.

GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype.

The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector.

Multi-Agent System for Rogue Drone Interception

A multitude of applications based on unmanned aircraft vehicles (UAVs), in conjunction with the UAVs' diminishing costs and increasing capabilities, present a major threat to public safety as well as critical infrastructure security, leading to the need for versatile and robust counter-drone solutions. This work presents a novel multi-agent jamming system (MUAV-JS), where a group of cooperative autonomous agents employ various algorithms (detection, tracking, jamming, and self-localization) to counter unauthorized drone operations. The proposed system employs a joint wireless jamming and cooperative positioning framework to best track and intercept the moving rogue drone, utilizing a relative positioning system based on signals of opportunity in conjunction with inertial and vision measurements. Software-defined radio technology is incorporated on the pursuer agent to achieve rogue drone GPS disruption, while at the same time the autonomous agents cooperate to compute the location estimate of the pursuer agent. In the presence of jamming, to improve the wireless communication performance of the autonomous agents, a cellular communication architecture is also used as an additional channel for control and information exchange. For evaluation purposes, a prototype multi-agent system is designed, implemented, and tested in a real-world environment to demonstrate its enhanced localization and jamming performance, when compared to single-agent approaches.

Mitigating Jamming Attack in 5G Heterogeneous Networks: A Federated Deep Reinforcement Learning Approach

Jamming attack is one of the serious security breaches in the upcoming fifth-generation heterogeneous networks (5G HetNets). Most of the existing anti-jamming techniques, such as frequency hopping (FH) and direct sequence spread spectrum (DSSS) lack in self-adaptive capabilities to mitigate the security and privacy issues in highly dynamic 5G HetNet environment. In literature, although reinforcement learning (RL) has been explored a lot in designing various anti-jamming techniques to address the aforementioned problems, but these techniques suffer from the issue of large network resource consumption and slow convergence rate. To mitigate these issues, we propose a federated deep reinforcement learning (DRL) based anti-jamming technique for two-tier 5G HetNets. In the proposal, each femtocell of 5G HetNets is assumed to have multiple single antenna femto users (FUs) and a multi-antenna jammer used to jam the downlink signals from femto base station (FBS) to FUs. Aiming to improve the achievable rate at FUs in the presence of jammers, a joint optimization problem of beamforming and power allocation at FBSs is formulated by considering the quality-of-service (QoS) requirements of FUs. Due to the non-convex nature of the aforementioned optimization problem, we have used the Markov decision process (MDP) to transform the optimization problem into a multi-agent reinforcement learning (MARL) problem. Then, to solve this MDP with large number of states and action spaces, a federated deep reinforcement learning (DRL) scheme is proposed to maximize the achievable rate at FUs. The proposed scheme uses federated learning and dueling architecture of dueling double deep Q network (D3QN) to optimize the beamforming vectors and power allocation jointly at FBSs. The achievable rate performance of the proposed federated DRL scheme is compared with double deep Q network (DDQN) and deep Q network (DQN). Simulation results show that the proposed federated DRL scheme achieves 19.39% and 23.85% better achievable rate in comparison to DDQN and DQN schemes.

Maximizing the energy efficiency in intelligent reflecting surface‐aided unmanned aerial vehicle data collection system in presence of malicious jammers

AbstractIn this article, intelligent reflecting surface (IRS) aided an unmanned aerial vehicle (UAV) data collection system is investigated, where an IRS is deployed to assist in the communication from a cluster of Internet‐of‐Things (IoT) devices to a UAV in the presence of a jammer. We aim to improve the energy efficiency (EE) via the joint design of UAV trajectory, IRS passive beamforming, device power allocation and communication scheduling. However, the formulated nonlinear fractional programming problem is challenging to solve due to its nonconvexity and coupled variables. To overcome the difficulty, we propose an alternating optimization based algorithm to solve it sub‐optimally by leveraging the Dinkelbach's algorithm, successive convex approximation (SCA) technique, and block coordinate descent (BCD) method. Extensive simulation results show that the proposed design can significantly improve the anti‐jamming performance. In particular, for the remote jammer case, the proposed design can largely shorten the flight path and thus decrease the energy consumption via the signal enhancement; while for the local jammer case, which is deemed highly challenging in conventional systems without IRS since the retreating away strategy becomes ineffective, our proposed design even achieves a higher performance gain owing to the efficient jamming signal mitigation.

Robust Trajectory and Resource Optimization in UAV-Enabled IoT Networks under Probabilistic LoS Channel in Presence of Jammers

This paper studies the anti-jamming problem of unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) communication networks in the presence of a jammer under the accurate probabilistic line-of-sight (LoS) model. Our goal is to maximize the information collection throughput of the system under the assumption that only the jammer's approximate location is known. To this end, we formulate a throughput maximization problem by optimizing the UAV trajectory, the IoT devices' transmit power, and communication scheduling under the accurate real-time probabilistic LoS channel. However, the proposed optimization problem is non-convex and coupled, and hence intractable to be solved. In order to tackle the problem, a robust iterative algorithm is proposed by leveraging the block coordinate descent (BCD) method, the successive convex approximation (SCA) technology, the difference of convex (D.C) programming approach, and the S-procedure. Extensive simulation results show that our proposed algorithm significantly improves the system throughput while achieving a practical anti-jamming effect compared with the benchmark algorithms.

Jamming Threats in Free-Space Optics

Free space optical (FSO) technology has recently found a strong foothold in the telecommunication market due to its technical superiority and high-end applicability. Its technological advancement has caused rising implementation of FSO systems in defence, aerospace, and telecommunication industries. Although it is cutting edge, the fundamental benefits of this technology may be adversely affected by several foreign elements such as atmospheric turbulence, physical obstructions, and weather conditions. Along with these natural disrupting phenomena, for the deployment of FSO systems in security-constrained sectors like military and defence units, where extra data protection mechanism is needed, fabricated adversaries like jamming become feasible which is concerning for FSO applicability. It therefore becomes a challenging task to decipher legitimate FSO data in the presence of jammers and to secure an FSO system from jamming activities. On this account, a detailed discussion on jamming effects in FSO communication systems is showcased in this article. Also, the potential efforts to mitigate jamming in FSO networks are highlighted.

Optimal wireless rate and power control in the presence of jammers using reinforcement learning

Future wireless networks require high throughput and energy efficiency. This paper studies using Reinforcement Learning (RL) to do transmission rate and power control for maximizing a joint reward function consisting of both throughput and energy consumption. We design the system state to include factors that reflect packet queue length, interference from other nodes, quality of the wireless channel, battery status, etc. The reward function is normalized and does not involve unit conversion. It can be used to train three different types of agents: throughput-critical, energy-critical, and throughput and energy balanced. Using the NS-3 network simulation software, we implement and train these agents in an 802.11ac network with the presence of a jammer. We then test the agents with two jamming nodes interfering with the packets received at the receiver. We compare the performance of our RL optimal policies with the popular Minstrel rate adaptation algorithm: our approach can achieve (i) higher throughput when using the throughput-critical reward function; (ii) lower energy consumption when using the energy-critical reward function; and (iii) higher throughput and slightly higher energy when using the throughput and energy balanced reward function. Although our discussion is focused on 802.11ac networks, our method is readily applicable to other types of wireless networks.

High-Resolution Spectral Estimation for Continuous Wave Jamming Mitigation of GNSS Signals in Autonomous Vehicles

Future autonomous vehicles will rely mainly on global navigation satellite system (GNSS) receivers for positioning services. However, GNSS cannot maintain an accurate, continuous and reliable navigation solution in the presence of jamming. The widespread availability of in-car jammers or personal privacy devices (PPDs) made GNSS receivers an attractive target for signal jamming. Jammers not only jeopardize current positioning and timing services; they can indeed endanger the safety of the evolving intelligent transportation, autonomous road vehicles and critical infrastructure. The disruption of GNSS signal in some applications, especially safety-critical ones can lead to crucial consequences and risks such as loss of time synchronization and potential loss of life, to name a few. Thus, it is essential for future autonomous vehicles and transport service providers to deploy reliable GNSS anti-jamming techniques. This paper introduces a novel anti-jamming technique based on a high-resolution spectral estimation that utilizes fast orthogonal search (FOS) algorithm in which the jamming signal is modeled using a set of candidate functions and then eliminated from the received signal. The performance of the proposed method is assessed using experiments obtained from Spirent_TM GSS6700 simulation system. The jamming signal is either obtained from a real jammer or simulated using interference signal generator that is connected to Spirent_TM system. The results showed that the developed anti-jamming algorithm was able to successfully suppress the continuous wave (CW) interference signal, thus, the performance of the acquisition, tracking and navigation modules within a GPS software receiver were all enhanced.

Robust trajectory and communication design for angle-constrained multi-UAV communications in the presence of jammers

This paper studies a multi-unmanned aerial vehicle (UAV) enabled wireless communication system, where multiple UAVs are employed to communicate with a group of ground terminals (GTs) in the presence of potential jammers. We aim to maximize the throughput overall GTs by jointly optimizing the UAVs' trajectory, the GTs' scheduling and power allocation. Unlike most prior studies, we consider the UAVs' turning and climbing angle constraints, the UAVs' three-dimensional (3D) trajectory constraints, minimum UAV-to-UAV (U2U) distance constraint, and the GTs' transmit power requirements. However, the formulated problem is a mixed-integer non-convex problem and is intractable to work it out with conventional optimization methods. To tackle this difficulty, we propose an efficient robust iterative algorithm to decompose the original problem be three sub-problems and acquire the suboptimal solution via utilizing the block coordinate descent (BCD) method, successive convex approximation (SCA) technique, and S-procedure. Extensive simulation results show that our proposed robust iterative algorithm offers a substantial gain in the system performance compared with the benchmark algorithms.

Parameter Encoding for ECRB Minimization in the Presence of Jamming

Parameter Encoding for ECRB Minimization in the Presence of Jamming

Coverage probability of aerial heterogeneous networks disrupting dual connectivity in the Presence of Jammers

The coverage performance of the target user in dual connectivity-enabled heterogeneous networks is adversely affected depending upon the presence and distribution of wide-band jammers (WBJs). Thus, we deploy WBJs with uniform distribution using an independent homogeneous Poisson point process (IHPPP) and in clusters using the Matern cluster process (MCP). Results show that the coverage performance of the target user in the presence of IHPPP-distributed WBJs is larger than the MCP-distributed WBJs. Thus, to reduce the severity of jamming attacks, network designers should focus on the anti-jamming of clustered jammers relative to uniformly distributed jammers.

Robust trajectory planning for UAV communication systems in the presence of jammers

Unmanned Aerial Vehicle (UAV) has emerged as a promising novel application for the Sixth-Generation (6G) wireless communication by leveraging more favorable Line-of-Sight (LoS) propagation. However, the jamming resistance by exploiting UAV’s mobility is a new challenge in the UAV-ground communication. This paper investigates the trajectory planning problem in an UAV communication system, where the UAV is operated by a Ground Control Unit (GCU) to perform certain tasks in the presence of multiple jammers with imperfect power and location information. To ensure the reliability of the GCU-to-UAV link, we formulate the problem as a non-convex semi-infinite optimization, aiming to maximize the average worst-case Signal-to-Interference-plus-Noise Ratio (SINR) over a given flight duration by designing the robust trajectory of the UAV under stringent energy availability constraints. To handle this problem efficiently, we develop an iterative algorithm for the solution with the aid of S-procedure and Successive Convex Approximation (SCA) method. Numerous results demonstrate the efficacy of our proposed algorithm and offer some useful designinsights to practical system.

Multi-Layer Defences for Robust GNSS Timing Retrieval.

A multi-layered interference mitigation approach can significantly improve the performance of Global Navigation Satellite System (GNSS) receivers in the presence of jamming. In this work, three levels of defence are considered including: pre-correlation interference mitigation techniques, post-correlation measurement screening and Fault Detection and Exclusion (FDE) at the Position, Velocity, and Time (PVT) level. The performance and interaction of these receiver defences are analysed with specific focus on Robust Interference Mitigation (RIM), measurement screening through Lock Indicators (LIs) and Receiver Autonomous Integrity Monitoring (RAIM). The case of timing receivers with a known user position and using Galileo signals from different frequencies has been studied with Time-Receiver Autonomous Integrity Monitoring (T-RAIM) based on the Backward-Forward method. From the experimental analysis it emerges that RIM improves the quality of the measurements reducing the number of exclusions performed by T-RAIM. Effective measurements screening is also fundamental to obtain unbiased timing solutions: in this respect T-RAIM can provide the required level of reliability.

UAV Swarm Communication Under Malicious Jamming: Joint Trajectory and Clustering Design

In this letter, we investigate the UAV swarm communication in the presence of jammers, where cluster-based network architecture is adopted to enable efficient backhaul links. We aim to maximize the sum rate of the cluster head (CH) UAV by optimizing UAVs’ trajectories and clustering jointly subject to UAV’s various flying abilities. However, the formulated problem is non-convex and thus intractable to solve. Thus, based on block coordinate descent (BCD) and successive convex approximation (SCA) techniques, an iterative algorithm is proposed to solve this problem. Numerical results show that the proposed algorithm outperforms the benchmark algorithm for jointly designing the trajectory and clustering. Moreover, it also shows that the fixed-wing UAV is more favorable in anti-jamming UAV swarm communications, giving a certain practical significance.

Feasibility Study of UAV-Assisted Anti-Jamming Positioning

As the cost and technical difficulty of jamming devices continue to decrease, jamming has become one of the major threats to positioning service. Unfortunately, most conventional wireless positioning technologies are vulnerable to jamming attacks due to inherent shortcomings like weak signal strength and unfavorable anchor geometry. Thanks to their high operational flexibility, unmanned aerial vehicles (UAVs) could be a promising solution to the above challenges. Therefore, in this article, we propose a UAV-assisted anti-jamming positioning system, in which multiple UAVs first utilize time-difference-of-arrival (TDoA) measurements from ground reference stations and double-response two-way ranging (DR-TWR) measurements from UAV-to-UAV links to perform self-localization as well as clock synchronization, and then act as anchor nodes to provide TDoA positioning service for ground users in the presence of jamming. To evaluate the feasibility and performance of the proposed system, we first derive the Cramér-Rao lower bound (CRLB) of UAV self-localization. Then, the impacts of UAV position uncertainty and synchronization errors caused by jamming on positioning service are modeled, and the theoretical root-mean-square error (RMSE) of user position estimate is further derived. Numerical results demonstrate that the proposed system is a promising alternative to existing positioning systems when their services are disrupted by jamming. The most notable advantage of the proposed system is that it is fully compatible with existing user equipment terminals and positioning methods.

GPS Swept Anti-Jamming Technique Based on Fast Orthogonal Search (FOS)

Recently, there has been growing demand for GPS-based reliable positioning, with the broadening of a range of new applications that mainly rely on GPS. GPS receivers have, recently, been attractive targets for jamming. GPS signals are received below the noise floor. Thus, they are vulnerable to interference and jamming. A jamming signal can potentially decrease the SNR, which results in disruption of GPS-based services. This paper aims to propose a reliable and accurate, swept anti-jamming technique based on high-resolution spectral analysis, utilizing the FOS method to provide an accurate spectral estimation of the GPS swept jamming signal. resulting in suppressing the jamming signal efficiently at the signal processing stages in the GPS receiver. Experiments in this research are conducted using the SpirentTM GSS6700 simulation system to create a fully controlled environment to test and validate the developed method’s performance. The results demonstrated the proposed method’s capabilities to detect, estimate, and adequately suppress the GPS swept jamming signals. After the proposed anti-jamming module was employed, the software receiver was able to provide a continuous positioning solution during the presence of jamming within a 10 m positioning accuracy.

A Comparative Analysis of the Response of GNSS Receivers under Vertical and Horizontal L1/E1 Chirp Jamming.

Jamming is becoming a serious threat to various users of global navigation satellite systems (GNSS). Therefore, live monitoring tests are required to estimate the sensitivity range of GNSS receivers under jamming. This study analyses the response of some mass-market and professional-grade receivers to intentional interferences based on different 3D jammer positions. First, the vertical jamming was investigated, followed by a horizontal experiment where the receivers were placed at three locations while the jammer was moving within a triangular area. The aim was to determine a fingerprint of the influence of the L1/E1 chirp jammer on receivers used in the research. The results show that low-cost receivers are much more susceptible to interference, while the latest generation of GNSS geodetic receivers are much more resilient. It is encouraging that positioning in the presence of jamming could be achieved on a larger scale, especially by using professional receivers. An attempt to position the jammer will be left for trials when a more frequency stable device is applied.

Interference mitigation: impact on GNSS timing

While interference mitigation techniques can significantly improve the performance of a Global Navigation Satellite System (GNSS) receiver in the presence of jamming, they can also introduce distortions, biases and delays on the GNSS measurements and on the final receiver solution. We analyze the impact of five interference mitigation techniques on the solution provided by a GNSS timing receiver that operates in a known location and under static conditions. In this configuration, the receiver only estimates its clock bias and drift, which can be potentially affected by interference mitigation. The analysis has been performed considering a multiconstellation case, including GPS L1 Coarse Acquisition (C/A), Galileo E1b/c and Beidou B1c signals. Tests were also conducted on the wideband Galileo E5b modulation. In all cases, real jammers were used to challenge GNSS signal reception. The techniques analyzed are four Robust Interference Mitigation (RIM) approaches and the Adaptive Notch Filter (ANF). From the analysis, it emerges that RIM techniques do not affect the receiver clock bias and drift. On the other hand, the ANF introduces a modulation-dependent delay on the clock bias. This delay is difficult to predict and is common to signals adopting modulations with similar spectral characteristics. In this respect, interoperable signals such as the Galileo E1b/c and Beidou B1c components are affected in the same way by the ANF, which leaves the Galileo–Beidou intersystem bias unaltered. Stability analysis has also been performed: interference mitigation does not significantly increase the short-term characteristics of the estimated clock bias and drift for low jamming levels.

Learning to Coordinate in a Decentralized Cognitive Radio Network in Presence of Jammers

Efficient utilization of licensed spectrum in the cognitive radio network is challenging due to lack of coordination among the Secondary Users (SUs). Distributed algorithms proposed in the literature aim to maximize the network throughput by ensuring orthogonal channel allocation for the SUs. However, these algorithms work under the assumption that all the SUs faithfully follow the algorithms which may not always hold due to the decentralized nature of the network. In this paper, we study distributed algorithms that are robust against malicious behavior (jamming attack). We consider both the cases of jammers launching coordinated and uncoordinated attacks. In the coordinated attack, the jammers select non-overlapping channels to attack in each time slot and can significantly increase the number of collisions for SUs. We setup the problem in each scenario as a multi-player bandit and develop algorithms. The analysis shows that when the SUs faithfully implement proposed algorithms, the regret is constant with high probability. We validate our claims through exhaustive synthetic experiments and also through a realistic USRP based experiment.