Jamming Patterns Research Articles

Efficient Jamming Identification in Wireless Communication: Using Small Sample Data Driven Naive Bayes Classifier

The identification of malicious jamming pattern is significant for wireless communication system to adopt targeted anti-jamming approaches. Efficient identification of malicious jamming patterns requires sufficient sample data and computing resources for the complex electromagnetic environment. However, with the use of portable communication equipment and emergency communications, it is difficult to ensure adequate sample data and computing resources. Against this background, this letter focuses on the jamming identification based on a small sample data-driven Naive Bayes classifier. The main idea is to obtain approximate conditional feature probability via data augment and kernel density estimation (KDE) from a small sample labelled data and construct Naive Bayes classifier, so as to form a fast jamming recognition method. Simulation results show that compared with the methods using decision tree or simple neutral network, the proposed scheme achieves a better average accuracy between six jamming patterns, as well as low-complexity.

Intelligent Anti-Jamming Communication for Wireless Sensor Networks: A Multi-Agent Reinforcement Learning Approach

In this article, we investigate intelligent anti-jamming communication method for wireless sensor networks. The stochastic game framework is introduced to model and analyze the multi-user anti-jamming problem, and a joint multi-agent anti-jamming algorithm (JMAA) is proposed to obtain the optimal anti-jamming strategy. In intelligent multi-channel blocking jamming environment, the proposed JMAA adopts multi-agent reinforcement learning to make online channel selection, which can effectively tackle the external malicious jamming and avoid the internal mutual interference among sensor nodes. The simulation results show that, the proposed JMAA is superior to the frequency-hopping method, the sensing-based method and the independent reinforcement learning. Specifically, the proposed JMAA has the higher average packet receive ratio than both the frequency-hopping method and the sensing-based method. Compared with the independent reinforcement learning, JMAA has faster convergence rate when reaching the same performance of average packet receive ratio. In addition, since the JMAA does not need to model the jamming patterns, it can be widely used for combating other malicious jamming such as sweep jamming and probabilistic jamming.

An unbiased carrier-phase anti-interference filter based on mirror frequency amplitude compensation

Anti-interference and high-precision measurement are two important indicators of the performance of a satellite navigation receiver. However, current receiver designs do not simultaneously satisfy these two criteria. While the carrier-phase ranging technique is necessary for high-precision receivers, frequency domain interference suppression (FDIS) results in tracking error biases for nonideal analog receiver channels. Importantly, as the FDIS filter is adaptive, the bias will vary with the jamming pattern, particularly when the frequency of interference varies. For precision navigation applications, this bias must be mitigated. Therefore, a new FDIS filter based on the mirror frequency amplitude compensation (MFAC) method is proposed in this paper. The amplitude at the symmetry position of the notch frequency is doubled in the MFAC method to mitigate this carrier-phase bias. The simulation results showed that the MFAC method can reduce the range of the carrier-phase bias by more than 60% for different interference bandwidths, which substantially exceeds that achieved using the conventional FDIS and calibration filter methods of 20 orders.

JRNet: Jamming Recognition Networks for Radar Compound Suppression Jamming Signals

As electromagnetic environments in battlefields are more and more complex, there are more kinds of suppression jamming noise including both single jamming signals and compound jamming signals. Thus, suppression jamming recognition especially for compound jamming signals is becoming vital and challenging. Whereas conventional methods are prone to owning the low recognition accuracy and the high computational complexity, especially under low jamming-to-noise ratio (JNR) conditions. In this paper, a novel jamming recognition network (JRNet) based on robust power-spectrum features is proposed to recognize ten kinds of suppression jamming signals including four single jamming patterns and six compound jamming patterns. The proposed method combines the significant representative power of the JRNet and distinguished power-spectrum features of jamming signals to promote the recognition performance. By integrating residual blocks and asymmetric convolution blocks, the JRNet is capable to address the degradation problem and enhance the recognition ability for subtle features. The simulation results show that the overall recognition accuracy of the proposed method is more than 90% even when the JNR is −18 dB and is close to 100% at −6 dB. Compared with five comparison methods in recent literatures, the proposed JRNet achieves better and stable recognition performance especially under low JNR conditions with relatively less storage source and a bit more FLOPs and inference time.

Convert Harm Into Benefit: A Coordination-Learning Based Dynamic Spectrum Anti-Jamming Approach

This paper mainly investigates the multi-user anti-jamming spectrum access problem. Using the idea of “converting harm into benefit,” the malicious jamming signals projected by the enemy are utilized by the users as the coordination signals to guide spectrum coordination. An “internal coordination-external confrontation” multi-user anti-jamming access game model is constructed, and the existence of Nash equilibrium (NE) as well as correlated equilibrium (CE) is demonstrated. A coordination-learning based anti-jamming spectrum access algorithm (CLASA) is designed to achieve the CE of the game. Simulation results show the convergence, and effectiveness of the proposed CLASA algorithm, and indicate that our approach can help users confront the malicious jammer, and coordinate internal spectrum access simultaneously without information exchange. Last but not least, the fairness of the proposed approach under different jamming attack patterns is analyzed, which illustrates that this approach provides fair anti-jamming spectrum access opportunities under complicated jamming pattern.

Dynamic Spectrum Anti-Jamming in Broadband Communications: A Hierarchical Deep Reinforcement Learning Approach

In this letter, the frequency selection problem in jamming environment with large number of optional frequencies is investigated. With numerous optional actions in the wider frequency band scenario, most of existing anti-jamming methods will become ineffective, since the convergence time and computational complexity will grow exponentially with the number of actions. To cope with the above challenge, a novel hierarchical deep reinforcement learning algorithm which does not need to know the jamming patterns and channel model is proposed. The proposed algorithm divides the frequency selection problem in the broadband into two steps via two subnetworks: Firstly, the frequency band is selected by the band selection network, and then the specific frequency is selected in this frequency band by the frequency selection network. Simulation results show that the proposed algorithm avoids multiple different jammings effectively and achieves satisfactory throughput with less calculation.

Deceptive Jamming Recognition Based on Wavelet Entropy And RBF Neural Network

The development of digital radio frequency memory has made the technology of deceptive jamming get a qualitative leap. The real-time and immersiveness are greatly improved, which makes it difficult to manually judge the type of deceptive jamming. Aiming at the problem of automatic identification of deceptive jamming, a method based on wavelet entropy for deceptive jamming is studied. Firstly, three mathematical models of deceptive jamming patterns are combed, then wavelet analysis is performed on the signal and wavelet entropy is extracted, and then sent to the radial basis function (RBF) neural network for automatic classification. The simulation results show that the proposed method has a high recognition rate for deceptive jamming patterns, which relatively less affected by the Jamming-to-Noise Ratio (JNR), and has a faster calculation velocity and better real-time performance.

A Countermeasure Against Random Pulse Jamming in Time Domain Based on Reinforcement Learning

Pulse jamming is one of the common malicious jamming patterns that can significantly reduce the of wireless communication's reliability. This paper investigates the problem of anti-jamming communication in a random pulse jamming environment. In order to obtain the countermeasure in time domain, the Markov decision process (MDP) is employed to model and analyze the above problem, and a time-domain anti-pulse jamming algorithm (TDAA) based on reinforcement learning is proposed. The proposed algorithm learns from the dynamic interaction with the jamming environment to gradually approximate the optimal time-domain strategy. The optimal strategy enables the transmitter to switch between two states, i.e. “active” and “silent”, to avoid random pulse jamming. In addition, a state estimation and adjustment method for the random pulse jamming environment is introduced to improve the robustness of the proposed TDAA. Simulation results show that, compared with continuous transmission, the proposed TDAA can effectively reduce the jamming collision ratio and significantly improve the normalized throughput. And compared with transmitting terminal Q-learning algorithm (TTQA), the proposed TDAA has higher time utilization ratio and normalized throughput.

Mid-infrared laser source for testing jamming code effectiveness in the field

We report a mid-infrared (mid-IR or 3.5 to 5.0 μm) laser source for testing jamming code effectiveness against heat-seeking missiles in open-field conditions. We developed an optical parametric oscillator (OPO) based on a ZnGeP2 (ZGP) crystal for converting a 2.1-μm pump laser into mid-IR. The pump laser is an acousto-optically Q -switched Ho:YAG laser pumped by a continuous-wave (cw) Tm:fiber laser. The maximum possible average mid-IR power obtained is 6.5 W at a pump power of 20 W with a 33% power conversion efficiency. The pump pulses resulting from the Q -switched operation of the Ho:YAG laser have a pulse width of 30 ns (FWHM) at a pulse repetition frequency (PRF) of 50 kHz. We characterized the output beam in terms of its output power and wavelength spectrum. The output power of the pump laser can be modulated in a pattern of on/off pulses with a smaller frequency than the PRF. The frequency and the duty cycle of these on/off pulses can be adjusted. Identical on/off pulse modulation appears on the mid-IR output beam after the OPO. It is possible to create jamming patterns and codes in mid-IR band using this feature. We have presented examples of on/off modulation of the OPO output at arbitrary pattern frequencies and duty cycles.

Modeling and Simulation of Three Distributed Cooperative Suppressive Jamming Modes

The distributed cooperative jamming based on joint modes is an effective means to counter the multi-function radar or netted radar, different joint modes will bring different jamming effects. This paper, analyzed two common suppressive jamming patterns, including noise convolution jamming and dense false target jamming, and presented the simulation suppressive effect aiming at LFM radar; illustrated the conception of cooperative jamming and cooperative mode, put forward three new cooperative modes and carried out theoretic analyses and simulation, the simulated results validated the cooperative jamming’ effectiveness.

Analytical solutions of pattern formation for a class of discrete Aw–Rascle–Zhang traffic models

A follow-the-leader model of traffic flow is considered in the framework of the discrete Aw–Rascle–Zhang model which is a combination of the nonlinear General Motors model and the Optimal Velocity model. In this model, which is studied on a closed loop, stable and unstable pulse or jam patterns emerge. Analytical investigations using truncated Fourier analysis show that the appearance of the jam patterns is due to supercritical Hopf bifurcations. These results are confirmed and supplemented by numerical simulations. In addition, a link between the discrete Aw–Rascle–Zhang model and the modified Optimal Velocity model is established.

Feature extraction and clustering analysis of highway congestion

Classification of congestion patterns is important in many areas in traffic planning and management, ranging from policy appraisal, database design, to prediction and real-time control. One of the key constraints in applying machine learning techniques for classification is the availability of sufficient data (traffic patterns) with clear and undisputed labels, e.g. traffic pattern X or Y. The challenge is that labelling traffic patterns (e.g. combinations of congested and freely flow areas over time and space) is highly subjective. In our view this means that assessment of how well algorithms label the data should also include a qualitative component that focuses on what the found patterns really mean for traffic flow operations and applications. In this study, we investigate the application of clustering analysis to obtain labels automatically from the data, where we indeed first qualitatively assess how meaningful the found labels are, and subsequently test quantitatively how well the labels separate the resulting feature space. By transforming traffic measurements (speeds) into (colored) images, two different approaches are proposed to extract the features of a large number of traffic patterns for clustering: point-based and area-based. The point-based approach is widely applied in the image processing literature, and explores local interest points in images (i.e. where large changes occur in color intensity); whereas a new area-based approach combines domain knowledge with Watershed segmentation to partition the images into different spatial-temporal segments from which domain specific features, such as wide moving jam patterns, are extracted. The results show that the Watershed segmentation separates the traffic (congestion) patterns into more meaningful and separable classes, comparable to those that have been proposed in the literature. Since there is no ground-truth set of labels, the quantitative assessment tests how well both methods are able to separate the respective feature spaces they construct for the (large) database of traffic patterns. We argue that the more crisp this separation is; the better the labelling has turned out. For this quantitative comparison we train a multinomial classifier that maps unseen patterns to the labels discovered by each of the two labeling approaches. The most important result is that the classifier using the area-based feature vector achieves the highest average levels of confidence in its decisions to classify patterns, implying a highly separable feature vector space. We argue this is good news! Not only does the combination of image processing (Watershed) and domain knowledge (traffic flow characteristics) lead to meaningful labels that can be automatically retrieved from large databases of data; this method also leads to a more efficient separation of the resulting feature space. Our next endeavor is to further refine and use this method to develop a search engine for the (rapidly growing) 200 TB historical database of traffic data hosted by the Dutch National Datawarehouse (NDW).

Pattern-Aware Intelligent Anti-Jamming Communication: A Sequential Deep Reinforcement Learning Approach

This paper investigates the problem of anti-jamming communication in dynamic and intelligent jamming environment. A sequential deep reinforcement learning algorithm (SDRLA) without prior information is proposed, and raw spectrum information is used as the input of SDRLA. The proposed SDRLA algorithm mainly contains two parts: Firstly, deep learning and sliding window principle are introduced to identify jamming patterns; Secondly, reinforcement learning is carried out to make on-line channel selection based on recognized jamming patterns. In addition, channel switching cost is introduced for the purpose of formulating the trade-off relationship between throughput and overhead. Taking advantage of both deep learning and reinforcement learning, this method can realize rapid and effective anti-jamming channel selection with no need for modeling the jammer's characteristics. Simulation results show the convergence and effectiveness of the proposed SDRLA algorithm. Compared with single-mode reinforcement learning, our approach can reach better convergence performance and higher utility.

Anti-Jamming Communications Using Spectrum Waterfall: A Deep Reinforcement Learning Approach

This letter investigates the problem of anti-jamming communications in dynamic and unknown environment through on-line learning. Different from existing studies which need to know (estimate) the jamming patterns and parameters, we use the spectrum waterfall, i.e., the raw spectrum environment, directly. Firstly, to cope with the challenge of infinite state of raw spectrum information, a deep anti-jamming Q-network is constructed. Then, a deep anti-jamming reinforcement learning algorithm is proposed to obtain the optimal anti-jamming strategies. Finally, simulation results validate the the proposed approach. The proposed approach is relying only on the local observed information and does not need to estimate the jamming patterns and parameters, which implies that it can be widely used various anti-jamming scenarios.

A reduced bias delay lock loop for adaptive filters

Narrowband interferences (NBIs) severely degrade the quality of a received signal and can hinder the operation of GPS receivers, and therefore, they are commonly excised using an adaptive transversal filter. This filter does not cause code tracking bias in the case of an ideal analog receiver channel when its magnitude and phase response are constant; however, distortion is induced by RF cables, amplifiers, and mixers that results in an asymmetric correlation function. This correlation function is further deformed by the adaptive transversal filter, resulting in a nonzero bias. Given the adaptive nature of this transversal filter, the bias varies based on the jamming pattern. For precision navigation applications, this bias must be mitigated. With this problem in mind, a new technique called amplitude estimating delay lock loop (AEDLL) is presented. By using data related to a known structure of the adaptive transversal filter, the proposed method only needs to estimate the amplitude of the correlation function and revise the correlation function for code tracking. Simulations show that the AEDLL method is capable of reducing the RMSE of code tracking bias to less than 0.12ns, which is significantly smaller than that achieved using existing methods.

Jamming mechanism on the scale-free network with heterogeneous node capacity

To understand how the jamming on real communication networks depends on node capacity, we study the traffic model with heterogeneous node capacity. In this model, each movable packet takes a biased random walk and the capacity of a node with degree k is given as C(k) ∼ k x with a tunable parameter x. Each packet disappears when it arrives at the preassigned target node. We analytically and numerically show that the transition from the free-flow phase to the jammed phase occurs when the balance between the packet generations and removals is broken. The balance breaking condition for the jamming is analytically determined by the competition between C(k) and the average number of packets on a node of degree k, m f (k), in the free-flow phase. Based on the analytic arguments, we find that there exist three different jamming patterns depending on C(k). The analytic conjectures for jamming patterns are verified by numerical simulations.

SimpleMAC: A Simple Wireless MAC-Layer Countermeasure to Intelligent and Insider Jammers

In wireless networks, users share a transmission medium. For efficient channel use, wireless systems often use a Medium Access Control (MAC) protocol to perform channel coordination by having each node announce its usage intentions and other nodes avoid making conflicting transmissions. Traditionally, such announcements are made on a common control channel. However, this control channel is vulnerable to jamming because its location is pre-assigned and known to attackers. Furthermore, the announcements themselves provide information useful for jamming. We focus on a situation where transmitters share spectrum in the presence of intelligent and insider jammers capable of adaptively changing their jamming patterns. Despite the complex threat model, we propose a simple MAC scheme, called SimpleMAC, that effectively counters network compromise and MAC-aware jamming attacks. We then study the optimal adversarial behavior and analyze the performance of the proposed scheme theoretically, through Monte Carlo simulations, and by implementation on the WARP software-defined radio platform. In comparison to the Nash equilibrium alternative of disabling the MAC protocol, SimpleMAC quickly attains vastly improved performance and converges to the optimal solution (over six-fold improvement in SINR and 50% gains in channel capacity in a realistic mobile scenario).

JAMMING TRANSITION OF POINT-TO-POINT TRAFFIC THROUGH CO-OPERATIVE MECHANISMS

We study the jamming transition of two-dimensional point-to-point traffic through co-operative mechanisms (DCM) using computer simulation. We propose two decentralized co-operative mechanisms CM which are incorporated into the point-to-point traffic models: stepping aside (CM-SA) and choosing alternative routes (CM-CAR). Incorporating CM-SA is to prevent a type of ping-pong jumps from happening when two objects standing face-to-face want to move in opposite directions. Incorporating CM-CAR is to handle the conflict when more than one object competes for the same point in parallel update. We investigate and compare four models mainly from fundamental diagrams, jam patterns and the distribution of co-operation probability. It is found that although it decreases the average velocity a little, the CM-SA increases the critical density and the average flow. Despite increasing the average velocity, the CM-CAR decreases the average flow by creating substantially vacant areas inside jam clusters. We investigate the jam patterns of four models carefully and explain this result qualitatively. In addition, we discuss the advantage and applicability of decentralized co-operation modeling.

Congestion of point-to-point mobile objects

Traffic congestion in point-to-point movement is studied using four simple traffic models. The four traffic models studied are movement between randomly chosen starting and destination points, back and forth movement between same two points, movement with preference of horizontal and vertical paths, and movement along shortest paths. The congestion properties of these traffic models are studied using computer simulation. It is found that the first and the third model has a sudden phase transition from free flow to jammed state at a critical system density, the second model has a gradual transition from free flow to jammed state, and the fourth model results in a simple deadlock. The phase transition properties and the critical densities are studied in detail. The emerging traffic jam patterns of the traffic models are also presented.

Towards a macroscopic modeling of the complexity in traffic flow.

Based on the assumption of a safe velocity U(e)(rho) depending on the vehicle density rho, a macroscopic model for traffic flow is presented that extends the model of the Kühne-Kerner-Konhäuser by an interaction term containing the second derivative of U(e)(rho). We explore two qualitatively different forms of U(e): a conventional Fermi-type function and, motivated by recent experimental findings, a function that exhibits a plateau at intermediate densities, i.e., in this density regime the exact distance to the car ahead is only of minor importance. To solve the fluid-like equations a Lagrangian particle scheme is developed. The suggested model shows a much richer dynamical behavior than the usual fluid-like models. A large variety of encountered effects is known from traffic observations, many of which are usually assigned to the elusive state of "synchronized flow." Furthermore, the model displays alternating regimes of stability and instability at intermediate densities. It can explain data scatter in the fundamental diagram and complicated jam patterns. Within this model, a consistent interpretation of the emergence of very different traffic phenomena is offered: they are determined by the velocity relaxation time, i.e., the time needed to relax towards U(e)(rho). This relaxation time is a measure of the average acceleration capability and can be attributed to the composition (e.g., the percentage of trucks) of the traffic flow.