Unknown Jamming Research Articles

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.

Optimization-Driven DRL-Based Joint Beamformer Design for IRS-Aided ITSN Against Smart Jamming Attacks

This paper investigates an intelligent reflecting surfaces (IRS) aided anti-jamming communication strategy in the integrated terrestrial-satellite network (ITSN), where the IRS is exploited to mitigate jamming interference and enhance the integrated system communication performance. In such a network, the terrestrial network and satellite network are co-existing with a spectrum-sharing scheme in the presence of a multi-antenna jammer. We aim at maximizing the weighted sum rate (WSR) of all users by jointly optimizing the terrestrial beamformers and IRS phase shifts while considering the signal-to-interference-plus-noise ratio (SINR) requirements of legitimate users. Different from the non-convex optimization techniques utilized in the IRS-related problem, a novel optimization-driven deep reinforcement learning (DRL) algorithm is proposed, which leverages both the robustness of model-free learning approaches and the efficiency of model-based optimization methods. In the optimization module of the proposed algorithm, we analyze the smart jammer under the unknown jamming model and derive a lower bound of the anti-jamming uncertainty, such that the IRS-aided anti-jamming problem can be solved by alteration method with second-order cone programming (SOCP) algorithm and semidefinite relaxation (SDR) technique. Simulation results demonstrate that the IRS can enhance the anti-jamming performance efficiently, and the proposed optimization-driven DRL algorithm can improve both the learning rate and the system performance compared with existing solutions.

Radar Active Jamming Recognition under Open World Setting

To address the issue that conventional methods cannot recognize unknown patterns of radar jamming, this study adopts the idea of zero-shot learning (ZSL) and proposes an open world recognition method, RCAE-OWR, based on residual convolutional autoencoders, which can implement the classification of known and unknown patterns. In the supervised training phase, a residual convolutional autoencoder network structure is first constructed to extract the semantic information from a training set consisting solely of known jamming patterns. By incorporating center loss and reconstruction loss into the softmax loss function, a joint loss function is constructed to minimize the intra-class distance and maximize the inter-class distance in the jamming features. Moving to the unsupervised classification phase, a test set containing both known and unknown patterns is fed into the trained encoder, and a distance-based recognition method is utilized to classify the jamming signals. The results demonstrate that the proposed model not only achieves sufficient learning and representation of known jamming patterns but also effectively identifies and classifies unknown jamming signals. When the jamming-to-noise ratio (JNR) exceeds 10 dB, the recognition rate for seven known jamming patterns and two unknown jamming patterns is more than 92%.

Open world recognition of communication jamming signals

To improve the recognition ability of communication jamming signals, Siamese Neural Network-based Open World Recognition (SNN-OWR) is proposed. The algorithm can recognize known jamming classes, detect new (unknown) jamming classes, and unsupervised cluseter new classes. The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset. On the one hand, the network is required to have the ability to distinguish whether two samples are from the same class. On the other hand, the latent distribution of known class is forced to approach their own unique Gaussian distribution, which is prepared for the subsequent open set testing. During the test, the unknown class detection process based on Gaussian probability density function threshold is designed, and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes. The simulation results show that when the jamming-to-noise ratio is more than 0dB, the accuracy of SNN-OWR algorithm for known jamming classes recognition, unknown jamming detection and unsupervised clustering of unknown jamming is about 95%. This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.

Semantic Learning for Analysis of Overlapping LPI Radar Signals

The increasingly complex radio environment may cause the received low probability of intercept (LPI) radar signals to overlap in time–frequency domains. Analyzing overlapping LPI radar signals requires identifying the modulation type and estimating the parameters of each component. Prior research performs overlapping signal analysis as a multistage task, where each stage is designed to perform a part of the task. The multistage system will increase the calculation burden and cannot be optimized as a whole. Instead, this article proposes a novel framework for analyzing overlapping signals in a single stage. Specifically, we develop a joint semantic learning deep convolutional neural network (JSLCNN) that jointly learns three tasks, i.e., feature restoration, modulation classification, and parameter regression. Since the whole cognitive pipeline is a single network, it can be optimized end-to-end directly on cognitive performance. To verify the validity of the proposed JSLCNN, numerous comparative experiments are carried out in terms of modulation recognition and parameter estimation of overlapping signals. Experimental results demonstrate that the JSLCNN has desirable extensibility for identifying unseen signal combinations and robustness against unknown jamming. Furthermore, we show that the JSLCNN outperforms other existing approaches in generic real-time parameter estimation for LPI radar signals.

Joint Transmissive and Reflective RIS-Aided Secure MIMO Systems Design Under Spatially-Correlated Angular Uncertainty and Coupled PSEs

This paper investigates a joint transmissive and reflective reconfigurable intelligent surfaces (RIS) -aided secure multiple-input multiple-output (MIMO) system, where both a RIS-assisted transmitter and a RIS-based reflector are deployed to defend against the simultaneous jamming attack and wiretapping threat. Our design focuses on maximizing the sum rate under the unknown jammer’s beamforming, joint RISs’ coupled phase shift errors (PSEs), and spatially-correlated angular channel uncertainties. Besides, we take into account the various quality-of-service (QoS) requirement constraints for guaranteeing the secure performance. Since the problem is non-convex and mathematically intractable, a new optimization framework is established to facilitate the solution development to the formulated problem. Specifically, armed with the Akaike information criterion, a novel diagonalization method is first proposed to estimate the unknown jamming covariance matrix. Then, a series of fractional-eliminated rate expressions is derived that facilitates the application of the proposed Double Deterministic Transformation (DDT) to tackle the coupled stochastic PSEs. Besides, regardless of the spatial correlation matrix, a general discretization method is proposed to convert the e spatially-correlatd angular uncertainties into a worst-case robust one. Subsequently, building upon the above transformations which transform the original problem into tractable one, a two-layer iterative Lagrange multiplier algorithm capitalizing a low-complexity dual method is proposed to obtain the globally optimal solution of the digital precoder, where the multiple QoS constraints are handled without iteration. Meanwhile, we develop a novel polyblock-based multiple penalty method to obtain the globally optimal solutions to RISs’ phase shifts which can simultaneously satisfy the multiple QoS constraints. Moreover, to address the narrow feasibility region induced by the multiple QoS constraints, a heuristic initial optimization method is proposed, which strengthens the existing result. Finally, theoretical analysis and numerical results demonstrate the optimality and the excellent performance of our proposed optimization framework.

Intelligent Radar Jamming Recognition in Open Set Environment Based on Deep Learning Networks

Jamming recognition is an essential step in radar detection and anti-jamming in the complex electromagnetic environment. When radars detect an unknown type of jamming that does not occur in the training set, the existing radar jamming recognition algorithms fail to correctly recognize it. However, these algorithms can only recognize this type of jamming as one that already exists in our jamming library. To address this issue, we present two models for radar jamming open set recognition (OSR) that can accurately classify known jamming and distinguish unknown jamming in the case of small samples. The OSR model based on the confidence score can distinguish known jamming from unknown jamming by assessing the reliability of the sample output probability distribution and setting thresholds. Meanwhile, the OSR model based on OpenMax can output the probability of jamming belonging to not only all known classes but also unknown classes. Experimental results show that the two OSR models exhibit high recognition accuracy for known and unknown jamming and play a vital role in sensing complex jamming environments.

Reinforcement Learning-Based Dynamic Anti-Jamming Power Control in UAV Networks: An Effective Jamming Signal Strength Based Approach

Unmanned aerial vehicle (UAV) assisted air-to-ground (A2G) communication is vulnerable to malicious jamming due to the broadcast nature of wireless communications. In this letter, an anti-jamming power control framework with an unknown jamming model and unknown transmission power is proposed. In particular, the probability density function (PDF) of the effective jamming signal strength (EJSS) is first estimated via kernel density estimation (KDE). Then, utilizing the EJSS, a deep deterministic policy gradient (DDPG) based framework is proposed to acquire the power control strategy in real time. Moreover, a trajectory design scheme based on K-means++ is proposed to track the location of users. The simulation results show that the proposed framework yields an improved sum rate and energy efficiency over the reference schemes.

A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology

Wireless communications are increasingly vulnerable to malicious jamming attacks due to the inherent openness and broadcast nature. In this regard, a novel anti-jamming scheme based on knowledge graph is proposed in this paper, where the knowledge graph which contains the prior knowledge of the commonly-used jamming patterns is utilized to make the fast anti-jamming decision with low complexity. Specifically, for the known jamming patterns, the anti-jamming strategy is directly extracted based on the search from the knowledge graph by using the sensing information. On the other hand, for the unknown jamming patterns, a time-frequency domain feature extraction method is first proposed to extract the jamming feature and match the jamming patterns in the knowledge graph. As such, multiple anti-jamming strategies can be obtained by the knowledge graph search. Finally, the optimal strategy can be achieved by the two communication decision criteria-based strategy fusion. The simulation results showed that the time-frequency domain feature extraction method can well extract the time-frequency domain feature of the jamming pattern, and the proposed algorithm has high decision accuracy and robustness.

Proceed From Known to Unknown: Jamming Pattern Recognition Under Open-Set Setting

In this letter, we study the problem of jamming pattern recognition (JPR) when some patterns have no training samples. Existing works were designed for recognition of known jamming patterns only, hence they failed to deal with unknown jamming patterns. To address this challenge, a zero-shot learning (ZSL) based JPR scheme is proposed. Firstly, a supervised training process is introduced to learn the latent feature representation of known jamming patterns. Then, an unsupervised classification approach is proposed to recognize different jamming patterns. Finally, both known and unknown jamming patterns are classified in latent feature space. Simulation results show that the proposed scheme achieves excellent performance when dealing with the open-set JPR tasks.

Adaptive subspace signal detection in a type of structure-nonhomogeneity environment

The nonhomogeneity of the environment usually degrades the detection performance of a detector. To overcome the problem of detecting a multichannel subspace signal embedded in structure-nonhomogeneity environment with unknown jamming, we assume the jamming belongs to a subspace which is orthogonal to the signal subspace in the stage of detector design. Moreover, resorting to the criterion of two-step Rao (2S-Rao) test, an adaptive detector is devised. Numerical experiments show that the proposed detector can provide better detection performance than other detectors both in the situations where the detector designed and other nonhomogeneous environment.

Design of Secure NOMA Against Full-Duplex Proactive Eavesdropping

We investigate the problem of secure non-orthogonal multiple access (NOMA) against full-duplex proactive eavesdropping, where the eavesdropper performs passive eavesdropping and active jamming simultaneously to interrupt the NOMA transmissions. To avoid the transmission outage caused by the unknown jamming level from the eavesdropper, we propose a novel transmission outage constrained scheme to limit the transmission outage probabilities of the users to a maximum tolerable threshold, which is also helpful in reducing the secrecy outage. We derive analytical expressions for the secrecy outage probability and secrecy diversity order to characterize the secrecy performance. Simulation results are provided to demonstrate the accuracy of the derived analytical results and the efficiency of the proposed scheme.

Statistical Performance Analysis of the Adaptive Orthogonal Rejection Detector

Besides noise and potential targets, there usually exists jamming, which can significantly degrade detection performance of a detector. In this letter, we analyze the statistical performance of the adaptive orthogonal rejection detector (AORD), recently proposed for the case of completely unknown jamming. We derive closed-form expressions for the probabilities of detection and false alarm and show how the jamming affects the detection performance. The theoretical results are verified by Monte Carlo (MC) simulations.

On Triggering Control of Single-Input Linear Systems Under Pulse-Width Modulated DoS Signals

This paper studies the stability of remotely controlled and observed single-input controllable linear systems under power-constrained pulse-width modulated denial-of-service (DoS) signals. The effect of a DoS jamming signal is to corrupt the communication channels, thus preventing the data from being received at its destination. In this work, we first assume that the DoS signal is partially known, i.e., a uniform lower bound for the off time intervals and the on-to-off transiting time instants are known. We then propose a first type of resilient control and triggering strategies which is provably capable of beating partially known jamming signals. Building on this, we then present our joint control and identification algorithms, ${\sc JAMCOID for Periodic Signals}$ and ${\sc JAMCOID}$, which are provably able to guarantee the system stability under unknown jamming signals. More precisely, the ${\sc JAMCOID for Periodic Signals}$ algorithm is able to partly identify a periodic DoS signal with known uniform lower bound for the off time intervals, whereas the ${\sc JAMCOID}$ algorithm is capable of dealing with power-constrained, but otherwise unknown, DoS signals while ensuring stability. The practicality of the proposed techniques is evaluated on a simulation example under partially known and unknown jamming scenarios.

Adaptive array detection in noise and completely unknown jamming

The presence of jamming usually degrades the detection performance of a detector. Moreover, sufficient information about the jamming may be difficult to be obtained. To overcome the problem of adaptive array signal detection in noise and completely unknown jamming, we temporarily assume the jamming belongs to a subspace which is orthogonal to the signal steering vector in the stage of detector design. Consequently, by resorting to the criteria of generalized likelihood ratio test (GLRT) and Wald test, we propose two adaptive detectors, which can achieve signal detection and jamming suppression. It is shown, by Monte Carlo simulations, that the two proposed adaptive detectors have improved detection performance over existing ones.

A Bandwidth expanding scheme for communication channels with noiseless feedback in the presence of unknown jamming noise

This paper treats the problem of transmitting a Gaussian discrete-time Markov process over a time-discrete additive white Gaussian channel with noiseless feedback which is subjected to statistically unknown jamming noise (satisfying a given power constraint). The channel is used more than once during the interval between the production of successive source letters, and the jamming noise is either allowed to be correlated with the encoder output, or forced to be totally independent of it. The complete solution obtained in the paper under a minimax criterion indicates that the optimum encoder-decoder structures are linear,and the least favorable jamming noise is a Gaussian process.