Jamming Scheme Research Articles

A Cross-Eye Jamming Scheme Based on Microwave Photonics and Its Hardware-in-Loop Performance Investigation

A Cross-Eye Jamming Scheme Based on Microwave Photonics and Its Hardware-in-Loop Performance Investigation

Game-Based Intelligent Jamming Strategy without Prior Information in Wireless Communications

Game-Based Intelligent Jamming Strategy without Prior Information in Wireless Communications

A cognitive communication jamming strategy based on Transformer and Deep Reinforcement Learning

The advent of sophisticated communication technologies, such as cognitive radio and anti-jamming techniques, has significantly elevated the challenge of disrupting enemy communications. Nevertheless, the inherent openness of wireless communications remains a vulnerability that can be exploited to interfere with them. Some contemporary Reinforcement Learning (RL)-based jamming strategies examine methods for rapidly identifying the optimal jamming strategy for a specific modulated signal. However, such algorithms lack the flexibility and responsiveness required to effectively counter the enemy’s evolving communication strategies. To address this issue, we propose a Transformer and Deep Reinforcement Learning (DRL)-based jamming strategy that can be trained to identify jamming methods for multiple digital and analog signals. In particular, the Transformer Encoder is employed as a network for DRL to process the state information pertaining to the enemy communication. Subsequently, the decision module of the Double Deep Q Network (DDQN) is utilized to select the jamming action based on the processed information. Furthermore, we have devised a reward function and constructed an invalid jamming list, with the objective of selecting an action that requires low power consumption and enhances the convergence speed of the algorithm. The experimental results demonstrate that the algorithm proposed in this paper exhibits notable performance advantages in comparison to other networks and DRL algorithms.

Multi-Node Joint Jamming Scheme for Secure UAV-Aided NOMA-CDRT Systems: Performance Analysis and Optimization

Unmanned aerial vehicle (UAV) communication using non-orthogonal multiple access-based coordinated direct and relay transmission (NOMA-CDRT) supports both massive connectivity and wide-area coverage, becoming a key technology for future emergency rescue communications. However, relay forwarding and high-quality line-of-sight links may subject UAV-aided NOMA-CDRT to multiple eavesdropping attempts by saboteurs. Therefore, we propose a multi-node joint jamming scheme using artificial noise (AN) for the UAV-assisted NOMA-CDRT to improve the system’s physical layer security. In the proposed scheme, the base station directly serves a nearby user while using a UAV relay to serve a disaster-affected user, and both the users and the UAV relay utilize AN to jointly interfere with eavesdroppers around the users. To accurately characterize and maximize the ergodic secrecy sum rate (ESSR) of the proposed scheme, we derive the corresponding closed-form expressions and design a joint power allocation and interference control (JPAIC) algorithm using particle swarm optimization. Simulations verify the correctness of the theoretical analysis, the ESSR advantage of the proposed scheme compared with the conventional NOMA-CDRT, and the effectiveness of the proposed JPAIC.

Efficient Jamming Policy Generation Method Based on Multi-Timescale Ensemble Q-Learning

With the advancement of radar technology toward multifunctionality and cognitive capabilities, traditional radar countermeasures are no longer sufficient to meet the demands of countering the advanced multifunctional radar (MFR) systems. Rapid and accurate generation of the optimal jamming strategy is one of the key technologies for efficiently completing radar countermeasures. To enhance the efficiency and accuracy of jamming policy generation, an efficient jamming policy generation method based on multi-timescale ensemble Q-learning (MTEQL) is proposed in this paper. First, the task of generating jamming strategies is framed as a Markov decision process (MDP) by constructing a countermeasure scenario between the jammer and radar, while analyzing the principle radar operation mode transitions. Then, multiple structure-dependent Markov environments are created based on the real-world adversarial interactions between jammers and radars. Q-learning algorithms are executed concurrently in these environments, and their results are merged through an adaptive weighting mechanism that utilizes the Jensen–Shannon divergence (JSD). Ultimately, a low-complexity and near-optimal jamming policy is derived. Simulation results indicate that the proposed method has superior jamming policy generation performance compared with the Q-learning algorithm, in terms of the short jamming decision-making time and low average strategy error rate.

Anti-Jamming Resource-Allocation Method in the EH-CIoT Network through LWDDPG Algorithm.

In the Energy-Harvesting (EH) Cognitive Internet of Things (EH-CIoT) network, due to the broadcast nature of wireless communication, the EH-CIoT network is susceptible to jamming attacks, which leads to a serious decrease in throughput. Therefore, this paper investigates an anti-jamming resource-allocation method, aiming to maximize the Long-Term Throughput (LTT) of the EH-CIoT network. Specifically, the resource-allocation problem is modeled as a Markov Decision Process (MDP) without prior knowledge. On this basis, this paper carefully designs a two-dimensional reward function that includes throughput and energy rewards. On the one hand, the Agent Base Station (ABS) intuitively evaluates the effectiveness of its actions through throughput rewards to maximize the LTT. On the other hand, considering the EH characteristics and battery capacity limitations, this paper proposes energy rewards to guide the ABS to reasonably allocate channels for Secondary Users (SUs) with insufficient power to harvest more energy for transmission, which can indirectly improve the LTT. In the case where the activity states of Primary Users (PUs), channel information and the jamming strategies of the jammer are not available in advance, this paper proposes a Linearly Weighted Deep Deterministic Policy Gradient (LWDDPG) algorithm to maximize the LTT. The LWDDPG is extended from DDPG to adapt to the design of the two-dimensional reward function, which enables the ABS to reasonably allocate transmission channels, continuous power and work modes to the SUs, and to let the SUs not only transmit on unjammed channels, but also harvest more RF energy to supplement the battery power. Finally, the simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods under multiple jamming attacks.

The Impact of Diverse Jamming Schemes against LTE-Based Remote Detonation Devices

The Impact of Diverse Jamming Schemes against LTE-Based Remote Detonation Devices

Topology optimization of UAV network for target surveillance task with support jamming

In this paper, a topology optimization method is proposed for cooperative surveillance in unmanned aerial vehicles (UAVs) under an interference environment. We introduce the regional coverage rate to evaluate the cooperative surveillance performance under interference. At its core, we derive the expression of detection probability under interference in detail, which is inherently tied to the signal to interference plus noise ratio (SINR) influenced by UAV configuration, in which the location of each UAV affects the cooperative surveillance performance. To maximize the network surveillance performance, we use the coverage rate metric as the objective function to formulate a topology optimization problem under the interference environment, while satisfying distance constraints between UAVs. As the problem is non-convex and highly nonlinear, the internal self-constrained particle swarm optimization (ISC-PSO) algorithm is used to solve this problem, which can well address the issue of range constraints while maintaining adequate convergence. The numerical studies demonstrate that the proposed algorithm can obtain good surveillance performance in terms of different jamming strategies and shapes of monitoring areas.

AK-MADDPG-Based Antijamming Strategy Design Method for Frequency Agile Radar.

Frequency agility refers to the rapid variation of the carrier frequency of adjacent pulses, which is an effective radar active antijamming method against frequency spot jamming. Variation patterns of traditional pseudo-random frequency hopping methods are susceptible to analysis and decryption, rendering them ineffective against increasingly sophisticated jamming strategies. Although existing reinforcement learning-based methods can adaptively optimize frequency hopping strategies, they are limited in adapting to the diversity and dynamics of jamming strategies, resulting in poor performance in the face of complex unknown jamming strategies. This paper proposes an AK-MADDPG (Adaptive K-th order history-based Multi-Agent Deep Deterministic Policy Gradient) method for designing frequency hopping strategies in frequency agile radar. Signal pulses within a coherent processing interval are treated as agents, learning to optimize their hopping strategies in the case of unknown jamming strategies. Agents dynamically adjust their carrier frequencies to evade jamming and collaborate with others to enhance antijamming efficacy. This approach exploits cooperative relationships among the pulses, providing additional information for optimized frequency hopping strategies. In addition, an adaptive K-th order history method has been introduced into the algorithm to capture long-term dependencies in sequential data. Simulation results demonstrate the superior performance of the proposed method.

A back‐to‐back coordination‐based learning scheme for deceiving reactive jammers in distributed networks

AbstractReactive jammers select jamming strategies according to the users’ responses; thus, conventional anti‐jamming methods such as frequency hopping are inadequate to defeat the jamming attack. In this article, the authors propose a novel uncoupled deception scheme to trap the reactive jammer into attacking a decoy channel in distributed networks. Specifically, the authors design a multi‐functional network utility for every user to mislead the jammer with a minimum energy consumption while achieving the highest network throughput. Based on the network utility, the anti‐jamming problem is formulated as an exact potential game such that the existence of Nash equilibrium can be guaranteed theoretically. The authors further propose a back‐to‐back coordination‐based learning algorithm to reach the optimal channel selection and power adaption in a non‐cooperative way. To alleviate the lack of mutual information exchange, the back‐to‐back coordination mechanism derives all users to deceive the jammer by inferring others’ strategies based on a shared belief. Simulation results show that the proposed algorithm yields higher network throughput and efficiency‐cost ratio compared to the state‐of‐the‐art cooperative schemes.

Jamming precoding in AF relay-aided PLC systems with multiple eavessdroppers

Enhancing information security has become increasingly significant in the digital age. This paper investigates the concept of physical layer security (PLS) within a relay-aided power line communication (PLC) system operating over a multiple-input multiple-output (MIMO) channel based on MK model. Specifically, we examine the transmission of confidential signals between a source and a distant destination while accounting for the presence of multiple eavesdroppers, both colluding and non-colluding. We propose a two-phase jamming scheme that leverages a full-duplex (FD) amplify-and-forward (AF) relay to address this challenge. Our primary objective is to maximize the secrecy rate, which necessitates the optimization of the jamming precoding and transmitting precoding matrices at both the source and the relay while adhering to transmit power constraints. We present a formulation of this problem and demonstrate that it can be efficiently solved using an effective block coordinate descent (BCD) algorithm. Simulation results are conducted to validate the convergence and performance of the proposed algorithm. These findings confirm the effectiveness of our approach. Furthermore, the numerical analysis reveals that our proposed algorithm surpasses traditional schemes that lack jamming to achieve higher secrecy rates. As a result, the proposed algorithm offers the benefit of guaranteeing secure communications in a realistic channel model, even in scenarios involving colluding eavesdroppers.

Against Jamming Attack in Wireless Communication Networks: A Reinforcement Learning Approach

When wireless communication networks encounter jamming attacks, they experience spectrum resource occupation and data communication failures. In order to address this issue, an anti-jamming algorithm based on distributed multi-agent reinforcement learning is proposed. Each terminal observes the spectrum state of the environment and takes it as an input. The algorithm then employs Q-learning, along with the primary and backup channel allocation rules, to finalize the selection of the communication channel. The proposed algorithm designs primary and backup channel allocation rules for sweep jamming and smart jamming strategies. It can predict the behavior of jammers while reducing decision conflicts among terminals. The simulation results demonstrate that, in comparison to existing methods, the proposed algorithm not only enhances data transmission success rates across multiple scenarios but also exhibits superior operational efficiency when confronted with jamming attacks. Overall, the anti-jamming performance of the proposed algorithm outperforms the comparison methods.

GA-Dueling DQN Jamming Decision-Making Method for Intra-Pulse Frequency Agile Radar.

Optimizing jamming strategies is crucial for enhancing the performance of cognitive jamming systems in dynamic electromagnetic environments. The emergence of frequency-agile radars, capable of changing the carrier frequency within or between pulses, poses significant challenges for the jammer to make intelligent decisions and adapt to the dynamic environment. This paper focuses on researching intelligent jamming decision-making algorithms for Intra-Pulse Frequency Agile Radar using deep reinforcement learning. Intra-Pulse Frequency Agile Radar achieves frequency agility at the sub-pulse level, creating a significant frequency agility space. This presents challenges for traditional jamming decision-making methods to rapidly learn its changing patterns through interactions. By employing Gated Recurrent Units (GRU) to capture long-term dependencies in sequence data, together with the attention mechanism, this paper proposes a GA-Dueling DQN (GRU-Attention-based Dueling Deep Q Network) method for jamming frequency selection. Simulation results indicate that the proposed method outperforms traditional Q-learning, DQN, and Dueling DQN methods in terms of jamming effectiveness. It exhibits the fastest convergence speed and reduced reliance on prior knowledge, highlighting its significant advantages in jamming the subpulse-level frequency-agile radar.

A Soft-Handoff-Based Cooperative Jamming Scheme for Security in Mobility Scenarios

A Soft-Handoff-Based Cooperative Jamming Scheme for Security in Mobility Scenarios

Sensing Jamming Strategy From Limited Observations: An Imitation Learning Perspective

Sensing Jamming Strategy From Limited Observations: An Imitation Learning Perspective

Intelligent Jamming Strategy for Wireless Communications Based on Game Theory

Intelligent Jamming Strategy for Wireless Communications Based on Game Theory

Jamming effect analysis and reticle parameter identification of infrared spin-scan seeker under dynamic engagement environment

The infrared jammer is a common countermeasure for escaping the missile’s attack, but a systemic analysis for jamming effectiveness in the target-missile engagement is absent. This paper analytically elaborates on the jamming effect for a spin-scan seeker with different parameters including jamming power, frequency, and phase. First, the mathematical expression of error signal for spin-scan seeker is derived, which provides the basis for constructing a missile guidance loop in which a target at arbitrary orientation employs a jammer. Next, the jamming effect of infrared jammer with different parameters is evaluated by the magnitude of miss distance. The simulation results and corresponding theoretical analysis indicate that a greater jamming power will increase the target survival probability and the range of jamming frequency and phase can be set more flexibly. Furthermore, the jamming will be valid if the jamming frequency is close to the reticle spin frequency under the condition of a greater jamming power, which forms the jamming strategy. Finally, a frequency identification method for a missile is designed by the cat-eye effect theory through signal processing and proved to be accurate, so the jamming strategy will be more utilizable in practice. The simulation results and analysis in the present paper will provide theoretical support for infrared jamming strategy.

On the Security and Reliability Trade-off of the Satellite Terrestrial Networks with Fountain Codes and Friendly Jamming

The performance of the satellite-terrestrial network with Fountain codes (FCs) is conducted in the present work. More precisely, the air-to-ground link is modeled according to the shadow-Rician distribution to capture the strong light-of-sight (LOS) path as well as the impact of the shadowing. As a result, we employ the directional beamforming at both the satellite and relay to mitigate such an ultra-long transmission distance. We investigate the trade-off between the reliability and security aspects. Particularly, we derive the outage probability (OP) and intercept probability (IP) in the closed-form expressions. To further facilitate the security of the considered networks, the friendly jamming scheme is deployed as well. Finally, simulation results based on the Monte-Carlo method are given to corroborate the exactness of the developed mathematical framework and to identify key parameters such as antenna gain, and transmit power that have a big impact on the considered networks.

Joint optimization scheme for the reconfigurable intelligent surface-assisted untrusted relay networks

To further improve the secrecy rate, a joint optimization scheme for the reconfigurable intelligent surface (RIS) phase shift and the power allocation is proposed in the untrusted relay (UR) networks assisted by the RIS. The eavesdropping on the UR is interfered by a source-based jamming strategy. Under the constraints of unit modulus and total power, the RIS phase shift, the power allocation between the confidential signal and the jamming signal, and the power allocation between the source node and the UR are jointly optimized to maximize the secrecy rate. The complex multivariable coupling problem is decomposed into three sub-problems, and the non-convexity of the objective function and the constraints is solved with semi-definite relaxation. Simulation results indicate that the secrecy rate is remarkably enhanced with the proposed scheme compared with the equal power allocation scheme, the random phase shift scheme, and the no-RIS scheme.

A Novel Self-Exploration Scheme for Learning Optimal Policies against Dynamic Jamming Attacks in Cognitive Radio Networks

Abstract Cognitive Radio Networks (CRNs) present a compelling possibility to enable secondary users to take advantage of unused frequency bands in constrained spectrum resources. However, the network is vulnerable to a wide range of jamming attacks, which adversely affect its performance. Several countermeasures proposed in the literature require prior knowledge of the communication network and jamming strategy that are computationally intensive. These solutions may not be suitable for many real-world critical applications of the Internet of Things (IoT). Therefore, a novel self-exploration approach based on deep reinforcement learning is proposed to learn an optimal policy against dynamic attacks in CRN-based IoT applications. This method reduces computational complexity, without prior knowledge of the communication network or jamming strategy. A simulation of the proposed scheme eliminates interference effectively, consumes less power, and has a better Signal-to-Noise Ratio (SNR) than other algorithms. A platform-agnostic and efficient anti-jamming solution is provided to improve CRN’s performance when jamming occurs.