Channel Jamming Research Articles

Opponent-Awareness-Based Anti-Intelligent Jamming Channel Access Scheme: A Deep Reinforcement Learning Perspective

Opponent-Awareness-Based Anti-Intelligent Jamming Channel Access Scheme: A Deep Reinforcement Learning Perspective

Distributed Stable Multi-Source Dynamic Broadcasting for Wireless Multi-Hop Networks Under SINR-Based Adversarial Channel Jamming

Distributed Stable Multi-Source Dynamic Broadcasting for Wireless Multi-Hop Networks Under SINR-Based Adversarial Channel Jamming

Robust Beamforming for Sensing Assisted Integrated Communication and Jamming Systems

This paper investigates the robust beamforming design problem in a sensing assisted integrated communication and jamming (ICAJ) system, which aims to minimize the transmission energy consumption in a time duration while ensuring the communication requirements for multiple legitimate users and jamming demands for the suspicious network with multiple communication groups. To achieve these goals, we first design a sensing-communication and jamming (SCJ) protocol. Then, a robust beamforming design problem with both communication and jamming channel state information (CSI) estimation error is formulated as a non-convex optimization. To solve it, a joint optimization (JO) algorithm is developed to jointly optimize the beamforming matrices of communication and jamming, by leveraging the General S-procedure, Schur' complement and General Sign-definiteness techniques. Simulation results show that the proposed SCJ protocol with JO algorithm has superior performance, as compared to benchmark methods.

Learning-Based Multi-Domain Anti-Jamming Communication with Unknown Information

Due to the open nature of the wireless channel, wireless networks are vulnerable to jamming attacks. In this paper, we try to solve the anti-jamming problem caused by smart jammers, which can adaptively adjust the jamming channel and the jamming power. The interaction between the legitimate transmitter and the jammers is modeled as a non-zero-sum game. Considering that it is challenging for the transmitter and the jammers to acquire each other’s information, we propose two anti-jamming communication schemes based on the Deep Q-Network (DQN) algorithm and hierarchical learning (HL) algorithm to solve the non-zero-sum game. Specifically, the DQN-based scheme aims to solve the anti-jamming strategies in the frequency domain and the power domain directly, while the HL-based scheme tries to find the optimal mixed strategies for the Nash equilibrium. Simulation results are presented to validate the effectiveness of the proposed schemes. It is shown that the HL-based scheme has a better convergence performance and the DQN-based scheme has a higher converged utility of the transmitter. In the case of a single jammer, the DQN-based scheme achieves 80% of the transmitter’s utility of the no-jamming case, while the HL-based scheme achieves 63%.

Performance of Multi-Antenna Proactive Eavesdropping in 5G Uplink Systems

This paper studies the performance of multi-antenna proactive eavesdropping in 5G uplink systems with spatially correlated Rayleigh fadings, where the base station (BS) serves an illegal user and a multi-antenna legitimate monitor eavesdrops the suspicious link with the help of jamming attack. Based on a practical assumption of channel state information (CSI) in 5G uplink, i.e., imperfect instantaneous CSI of the suspicious link at the BS and that of eavesdropping link at the monitor, and only jamming channel statistics at the monitor, we first give a statistical jamming beamforming design. Then, semi-closed form expressions of eavesdropping non-outage probability and relative average eavesdropping rate are, respectively, derived for delay-sensitive and delay-tolerant scenarios. Via reasonable approximations and asymptotic analysis, we gain many insights on the effect of key system parameters, e.g., location-dependent channel path loss and angular spread, jamming energy and the number of antennas. Further, we provide the optimal energy allocation between pilot and data jamming and the optimal antenna allocation between jamming and eavesdropping under total energy constraint and total antenna number, respectively, which are both explicit functions of system parameters. Finally, simulation results validate our analytical results.

Anti-Jamming Communication Using Slotted Cross Q Learning

Most of the existing intelligent anti-jamming communication algorithms model sensing, learning, and transmission as a serial process, and ideally assume that the duration of sensing and learning timeslots is very short, almost negligible. However, when the jamming environment changes rapidly, the sensing and learning time can no longer be ignored, and the adaptability of the wireless communication system to the time-varying jamming environment will be significantly reduced. To solve this problem, this paper proposes a parallel Q-learning (PQL) algorithm. In the case of long sensing and learning time, by modeling sensing, learning, and transmission as parallel processes, the time that the transmitter remains silent during sensing and learning is reduced. Aiming at the situation that the PQL algorithm is susceptible to jamming when the jamming changes faster, this paper proposes an intelligent anti-jamming algorithm for wireless communication based on Slot Cross Q-learning (SCQL). In the case of rapid change of jamming channel, the system can sense and learn the jamming patterns in multiple successive jamming periods at the same time in the same timeslot, and use multiple Q-tables to learn the jamming patterns in different jamming periods, so as to achieve the effect of reliable communication in the environment with rapid change of jamming. The simulation results show that the jamming collision rate of the proposed algorithm under the condition of intelligent blocking jamming is equivalent to that of the traditional Q-learning (QL), but the timeslot utilization rate is higher. Compared with PQL, the proposed algorithm has the same slot utilization and lower jamming collision rate. Compared with random frequency hopping (RFH) anti-jamming, the proposed algorithm not only has higher timeslot utilization, but also has lower jamming collision rate.

Spectrum Compensation-Based Joint Communication and Jamming Channel Allocation: A Hierarchical Stochastic Potential Game Approach

No doubt both communication and jamming are indispensable in wireless confrontation. For the jamming side, a major challenge is how to coordinate the jamming and the internal communication behaviors. This paper investigates a joint channel strategy optimization problem in an integrated communication and jamming system. A spectrum compensation mechanism is proposed to coordinate the channel strategies between the users and the jammer within a Stackelberg game framework. Considering the dynamics of strategy space, we formulate the followers sub-game as a stochastic potential game. Moreover, we prove the existence of Stackelberg equilibrium and propose an algorithm to search for the equilibrium solution. Simulation results show that the proposed method can increase the jammer's and the users' utilities significantly compared with the uncoordinated situations.

A DRL-Based Intelligent Jamming Approach for Joint Channel and Power Optimization

The traditional jamming methods mainly focus on the optimization of a single domain, such as frequency domain. However, in the practical wireless communication, the jamming methods in single dimension domain are limited and difficult to cope with multidomain scenarios. To overcome these issues, this paper investigates the problem of joint decision-making for jamming channel and power in a dynamic spectrum environment. Firstly, the Markov decision process (MDP) is used to formulate the jamming channel and power joint selection problem. Then, a deep reinforcement learning- (DRL-) based jamming algorithm is proposed with the function of parallel learning and joint decision-making. Specially, to accelerate the learning speed of the algorithm, the prioritized experience reply (PER) technology is introduced. Finally, a practical jamming testbed is built to evaluate the proposed algorithm. The simulation results and the testbed results demonstrate the effectiveness of the proposed algorithm, which can guarantee the jamming effect and maximize the utilization of jamming resources.

Fight Against Smart Communication Rival: An Intelligent Jamming Approach With Trend-Oriented Efficacy Evaluation

This letter investigates a jamming problem in the confrontation scenario when both the opposites are intelligent. Most existing studies assumed the learning-based jammer could obtain the communication feedback as the jamming reward, which may be infeasible and limits the application. To overcome this limitation, we propose a jamming efficacy evaluation method based on the change of communication behaviors, which is oriented by the idea to reflect the trend of the real jamming effect approximatively. Considering the rival is intelligent to adjust the communication strategy dynamically, multi deep-learning classifiers are established by the jammer to predict the channel status and make the jamming channel strategy selectively, aiming to disturb the rival’s normal communication. Moreover, the jamming power strategy is optimized by multi-armed bandit (MAB) method within the energy constraint. Simulation results demonstrate the effectiveness of the jamming efficacy evaluation criteria and show that the proposed algorithm can decrease the rival’s throughput as well as saving the jamming cost.

A Coalition Formation Game-Based Multi-User Grouping Approach in the Jamming Environment

Due to the broadcast nature of wireless communication, it is vulnerable to malicious jamming attacks. Meanwhile, multiple users need to share the limited spectrum resources to enhance the spectrum utilization efficiency because of the scarcity of wireless spectrum resources. Therefore, wireless communication systems need to deal with the malicious jamming attacks and mutual interference among different users. In this paper, a multi-user anti-jamming grouping approach based on a coalition formation game is studied. The proposed approach can maximize users’ transmission rate while avoiding channel jamming through optimization of the coalition formation strategy under the influence of malicious jamming. With the help of the exact potential energy game, we demonstrate that the proposed game model can obtain stable Nash equilibrium solutions. Lastly, the effectiveness of the proposed algorithm is demonstrated through numerical.

A Smart Noise Jamming Suppression Method Based on Atomic Dictionary Parameter Optimization Decomposition

Smart noise jamming is a new jamming style against radar, which plays an important role in modern radar electronic warfare. In order to solve the complex problem of convolutional smart noise jamming suppression with different time delay and jamming frequency modulation, a novel jamming suppression method based on the optimized atomic dictionary decomposition by estimating significant jamming parameters is proposed in this paper. First, the dual channel jamming signal information is obtained by designing twinning waveforms, then the independent estimation of time delay and jamming frequency parameters are established based on the spatial position sequences of the false targets. After that, the atomic dictionary is optimized by utilizing the obtained jamming parameters to achieve more effective decomposition. The performance of the new jamming suppression method is analyzed and verified by experimental simulations. The results show that this method can feasibly and practicably suppress the smart noise jamming with a deceptive effect, and the atomic decomposition efficiency is highly improved.

Intelligent Jamming Defense Using DNN Stackelberg Game in Sensor Edge Cloud

To ensure an accurate power allocation against increasing intelligent jamming attacks on the offloading link of computation tasks, we investigate interactions between a cluster head node and an intelligent jammer using a Stackelberg game framework, under the constraint of the total power to use and the limited knowledge of its own channel gain for each player. In this game, the intelligent jammer gathers channel gain information and processes it using a deep neural network (DNN) to infer the accurate jamming power as an attack strategy. The cluster head node also exploits DNN to infer an accurate transmission power as a defense strategy according to the varying channel gain. We model the optimization of the attack and defense strategies using single channel jamming DNN (SJnet), multiple channel jamming DNN (MJnet), single channel sensor DNN (SSnet), and multiple channel sensor DNN (MSnet) for the single (multiple) channel jamming attacks. In addition, we extend the design to the scenario where the intelligent jammer can launch a hybrid mode jamming attack, and propose a DNN Stackelberg game-based defense scheme. Numerical simulation results demonstrate that our proposed mechanism is superior to other power allocation mechanisms under different scenarios in the sensor edge cloud.

Jamming attack against remote state estimation over multiple wireless channels: A reinforcement learning based game theoretical approach

In this paper, we design a potential game to investigate the interplay between sensors and attackers in wireless remote estimation systems (RESs), where we consider both closed-loop and open-loop scenarios that depend on whether the sensor’s actions are known to the attacker or not. As a typical application of Cyber–Physical Systems (CPSs), wireless RESs have been showing vulnerable to channel jamming attacks. As a countermeasure, the wireless sensor in the estimation system can switch to another independent wireless channel to transmit sensor data. However, a smart attacker can also switch the target channel to launch a jamming attack, resulting in a game between the sensor and attacker. Moreover, the reinforcement learning based methods are proposed to obtain solutions of the designed game models in two scenarios. We also provide extensive simulation results to validate the effectiveness of the proposed methods.

Performance of optical space shift keying under jamming.

In this paper, the effect of jamming in a free-space optical (FSO) communication system that employs optical space shift keying (OSSK) is studied. The optical link suffers from saturated atmospheric turbulence (AT). Hence, the channel fading coefficients follow negative exponential probability density function (PDF). Along with additive Gaussian noise, the jammer, under consideration, is an additional source of noise, and jamming signals also suffer from saturated AT. Thus, the jamming channel also follows the negative exponential PDF. A multiple-input single-output (MISO) FSO system is used to mitigate the effects of jammer and channel fading. Explicitly, we employ a 2×1 MISO-OSSK system for analysis and derive a closed-form expression of bit error rate (BER) of the considered system. Moreover, the jammer's effect is also studied numerically under the gamma-gamma (GG) fading channel.

A Survey of Cyberattack Countermeasures for Unmanned Aerial Vehicles

Unmanned aerial vehicle (UAV) has been increasingly used in a wide range of commercial and civilian applications. As an advanced cyber-physical system, UAVs are exposed to a wide range of cyberattacks. This paper first surveys existing literature for different cyberattacks. Then, we classify these attacks based on their attack entry points, which can be radio channels, messages or on-board systems. There are six classes of UAV cyberattacks, namely channel jamming, message interception, message deletion, message injection, message spoofing and on-board system attack. In the existing literature, there is no survey focusing on UAV cyberattack countermeasures. To close this gap, we survey existing countermeasures for the six attack classes. A comprehensive review of countermeasures is important because countermeasure may not be exclusive to an attack. Knowing a wide range of existing countermeasures can prepare us against existing and new cyberattacks. We classify countermeasure into three classes, namely prevention, detection and mitigation. Prevention countermeasures stop a cyberattack from starting. When prevention countermeasures fail, detection countermeasures alert UAV operator of an attack. After detecting an attack, mitigation countermeasures limits the damage. Following the survey, we further discuss the open challenges in developing countermeasures and propose some potential future research works.

AUTOMATIC MODULATION CLASSIFIER: REVIEW

The automatic modulation classification (AMC) is highly important to develop intelligent receivers in different military and civilian applications including signal intelligence, spectrum management, surveillance, signal confirmation, monitoring, interference identification, as well as counter channel jamming. Clearly, without knowing much information related to transmitted data and various indefinite parameters at receiver, like timing information, carrier frequency, signal power, phase offsets, and so on, the modulation’s blind identification has been a hard task in the real world situations with multi-path fading, frequency-selective in addition to the time-varying channels. There are 2 methods could be utilized to decide the classification signal technique: Feature-based (FB) approach and the Maximum likelihood functions (LB) method. With regard to the FB (referred to as pattern-recognition) classification method used in the study. In the presented work, thorough study is provided to find easy method to identify and classify the digital modulation signals at low SNRs. Spectral-based features, high-order statistic features, wavelet-based features, also cyclic features on the basis of cyclostationary typically utilized to determine and discriminate modulation types have been examined. The number of the classifiers which have been utilized in the process of discrimination have been studied thoroughly and compared for helping researchers in determining and finding the drawbacks with pattern-recognition according to past works. The presented study serving as guide with regard to studies of AMC for determining adequate algorithms and features.

The Resilience of Massive MIMO PNC to Jamming Attacks in Vehicular Networks

In this article, we investigate the resilience of Massive MIMO Physical Layer Network Coding (PNC) to jamming attack in both sub-6 GHz and millimeter-Wave (mmWave) systems in vehicular networks. Massive MIMO generally is resilient to jamming attacks, and we investigate the impact that PNC has on this resilience, if combined with Massive MIMO. The combination of Massive MIMO and PNC has shown a significant improvement in the bit error rate (BER) in our previous investigation. The corresponding framework is analysed against a barraging attack from a jammer, where the jamming channel is not known to the base station (BS), and the jammer can use any number of transmit antennas. Over Rayleigh channel, our simulation results reveal that Massive MIMO PNC performs better in the lower signal-to-noise ratio (SNR) regions to jamming attacks and this is achieved at twice the spectral efficiency. A similar performance is observed over mmWave channel.

Spectrum shaping using NC‐OFDM for cognitive radio applications

Cognitive radio (CR) is an innovative technology that supports dynamic spectrum access to utilise the spectrum more efficiently in an opportunistic manner without interfering with the licensed users. Multi-carrier modulation techniques such as orthogonal frequency division multiplexing (OFDM) is identified as one of the suitable candidates for the design of CR systems. To provide high data rates while avoiding interference with licensed users, a variant of OFDM called non-contiguous OFDM (NC-OFDM) is used in CR. NC-OFDM is useful for dynamic shaping of spectrum based on the channel impairments, interference and jamming threats. In this work, the customised NC-OFDM transceiver link is simulated and compared with the customised OFDM link in the MATLAB environment. It is implemented in Zynq-7000 System-on-Chip (SoC) 7Z020 with radio-frequency front-end board, AD-FMCOMMS3-EBZ Software Defined Radio platform. Implementation of this link is carried out on Spartan-3A FPGA and compared the hardware resource utilisation with Zynq platform. It is observed that the Zynq platform utilises less hardware resources for the implementation. The transmitter spectrum, spectrum after de-activating unwanted sub-carriers, the spectrum shaping using raised cosine filtering, spectrum sensing using the correlation of known pattern and FFT-based spectrum sensing are also simulated in the MATLAB.

A Direct Discrimination Packet Flow Based Improving Security against Reactive Traffic Attacks in Wireless Communication

The wireless networks is most difficult of selective filling attacks. If jammer localizations and resistance routing are left alone, both are very promising, and the service overhead is still below the real-time requirements. To propose a new Direct Discriminant Packet Flow Exploration [DDPFE] algorithm based on network utility maximization (NUM) to resolve the centralized reaction disturbance optimization problem in multi-source network without any lose to send the data. The impact of the networks is estimated through interference and combine these estimates with the ability to assign problems in this type of attacks. To overcome this type of physical-layer characterization of cryptographic primitives attacks using a Cooperative Crypto Riddle Hiding Algorithm (CCRHA) for the control channel jamming problem in-network, which takes advantage of the transfer the data using the Ad-hoc network. The resolution to detect the schema attacks and isolate the nodes for the threshold. CCRHA Presenting to find selectively invasive attackers in wireless networks. Multiple metrics are measured to detect areas of interference of the wireless network. Multi-measurement method considered Packet Delivery Ratio (PDR) and signals strength variation as parameters to detect the selective jamming attacks.

Multipath effect analysis and pre‐distortion processing for jamming on wideband ground radar through antenna sidelobe

The multipath transmission has a serious effect on jamming efficiency in a short distance ECM task for wideband ground radars. After the sidelobe gain model for a large antenna is introduced, the characteristics of multipath channel in time and frequency domain are analysed. The statistical characteristics about the amplitude change of the intercepted pulse signals and the occurrence conditions of frequency selective fading are explained. The short time compatibility between reconnaissance channel and jamming channel is used to pre-distort the jamming signal, and the serious influence of multipath transmission on the jamming signal is greatly eliminated. The fidelity of deception jamming on the radar is greatly improved. The validity of theoretical analysis is verified by simulation experiments. It provides an important theoretical guidance for a short distance electronic counter measures (ECM) task against ground radars.