Frequency Hopping Communication Research Articles

Classification and Identification of Frequency-Hopping Signals Based on Jacobi Salient Map for Adversarial Sample Attack Approach.

Frequency-hopping (FH) communication adversarial research is a key area in modern electronic countermeasures. To address the challenge posed by interfering parties that use deep neural networks (DNNs) to classify and identify multiple intercepted FH signals-enabling targeted interference and degrading communication performance-this paper presents a batch feature point targetless adversarial sample generation method based on the Jacobi saliency map (BPNT-JSMA). This method builds on the traditional JSMA to generate feature saliency maps, selects the top 8% of salient feature points in batches for perturbation, and increases the perturbation limit to restrict the extreme values of single-point perturbations. Experimental results in a white-box environment show that, compared with the traditional JSMA method, BPNT-JSMA not only maintains a high attack success rate but also enhances attack efficiency and improves the stealthiness of the adversarial samples.

MCMS submersibles: A safe undersea adventure

Abstract. Maritime Cruises Mini-Submarines (MCMS), a company based in Greece, aims to offer safe and deep-sea exploratory experiences to tourists. This paper discusses the primary challenges involved in ensuring tourist safety, particularly focusing on maintaining security in scenarios of mechanical failures or lost communication with the main vessel. We propose comprehensive safety protocols and technological solutions tailored for deep-sea tourist submersibles. The analysis begins with a detailed study of the forces acting on a submersible, considering both operational modes and the impact of ocean currents, utilizing a rigid-body coordinate system and Eulerian angular coordinate transformations to describe the submersibles motion comprehensively. We derive motion and safety parameters by solving the force differential equations, allowing the host ship to predict the submersibles position accurately. In addressing the recovery of lost submersibles, our approach evaluates power loss scenarios and search efficiency, recommending an optimal combination of hydroacoustic and frequency-hopping communication technologies for reliable, low-loss data transmission. We further enhance search methodologies by integrating motion parameters and ocean current dynamics to pinpoint probable locations, adjusting search strategies dynamically based on real-time data, and employing probabilistic models to optimize resource allocation during search operations. A case study in the Caribbean Sea exemplifies applying these methodologies, incorporating localized flow regimes and cooperative communication strategies among multiple submersibles to improve operational efficiency and safety.

Rydberg-atom-based multiband frequency-hopping communication receiver using five-level atomic system

Rydberg-atom-based multiband frequency-hopping communication receiver using five-level atomic system

Optimizing DSFH communication system performance via multi-feedback unsaturated tri-stable stochastic resonance for enhancement of periodic signal

To address the challenges in dual-sequence frequency-hopping (DSFH) communication caused by the difficulty in detecting intermediate-frequency (IF) signals under low signal-to-noise ratio conditions, this study proposes a method based on detecting enhanced periodic signals using a multi-feedback unsaturated tri-stable stochastic resonance (MFUTSR) system. Initially, we construct an unsaturated tri-stable stochastic resonance (UTSR) system by leveraging the anti-saturation characteristics of the segmented potential function. This UTSR system is then serially connected with a monostable system and incorporates feedback control to establish the MFUTSR system. Applying the adiabatic approximation theory, we derive the steady-state probability density (SPD) and signal-to-noise ratio (ISNR) of the MFUTSR system, investigating the influence of system parameters on these factors. Finally, to enhance the reliability of the DSFH communication system, we introduce a new evaluation metric, AC. Through numerical simulations and practical application in DSFH communication systems, it can be concluded that the MFUTSR system has significant advantages in periodic feature enhancement and noise suppression. Additionally, using AC as the evaluation metric for symbol decision can greatly reduce the system's bit error rate (BER). The innovative MFUTSR system introduced in this study enhances periodic signal detection in DSFH communication, demonstrating superior robustness against noise and significantly improving system reliability.

Intelligent decision-making for a “Three-Variable” frequency-hopping pattern based on OC-CDRL

The frequency hopping pattern of the existing frequency hopping communication system is not designed according to the electromagnetic interference environment, resulting in blind anti-jamming. Therefore, to address this problem, a “three-variable” frequency-hopping pattern is proposed, where the frequency, hopping rate, and instantaneous bandwidth of the frequency-hopping signal vary randomly based on the background electromagnetic interference. The decision-making problem of the “three-variable” frequency-hopping pattern is modeled as a Markov decision process (MDP) by constructing the state-action-reward tuple. The designed frequency varies continuously within a small frequency band selected from a pseudo-random sequence to alleviate the problem of dimension explosion in decision-making. At the same time, discrete values for the hopping rate and instantaneous bandwidth are designed. To solve this MDP problem efficiently, a combined deep reinforcement learning algorithm (OC-CDRL) based on optimistic exploration and conservative estimation is proposed, which combines the features of TD3 and D3QN algorithms and designs the corresponding states, actions, and rewards to deal with continuous and discrete action spaces, respectively. To address the problem that the D3QN algorithm tends to fall into local optimal solutions, an optimistic exploration strategy (OES) for action selection is proposed to improve the degree of exploration. Moreover, the loss function is improved by conservatively estimating state–action pairs outside the experience replay buffer, reducing the overestimation of the optimistic action-value function and increasing the stability and convergence of the algorithm. Comparative simulation results of the algorithms in different electromagnetic interference environments show that the OC-CDRL algorithm effectively avoids most regions with higher interference and has better adaptability and anti-jamming capability.

Adaptive Whitening and Feature Gradient Smoothing-Based Anti-Sample Attack Method for Modulated Signals in Frequency-Hopping Communication

Adaptive Whitening and Feature Gradient Smoothing-Based Anti-Sample Attack Method for Modulated Signals in Frequency-Hopping Communication

Construction of secure adaptive frequency hopping sequence sets based on AES algorithm

AbstractCommunication security has become particularly crucial with the rapid development of the Internet of Things (IoT). Frequency hopping spread spectrum (FHSS) technology, a prevalent method in wireless communication, has a wide range of applications in the Internet of Things. Enhancing the security of frequency hopping sequences is an essential means to improve the security of frequency hopping communication in the Internet of Things, as the performance of frequency hopping sequences plays a crucial role in frequency hopping systems. This paper proposes constructing secure adaptive frequency hopping sequence sets based on the advanced encryption standard (AES) algorithm. As a block cipher algorithm with superior security, the AES algorithm can provide a fundamental guarantee for the security of the proposed frequency hopping sequences. The mapping methods from ciphertext sequences to frequency hopping sequences proposed in this paper can achieve the construction of frequency hopping sequences of any frequency set size to meet the requirements of adaptive frequency hopping. In addition, we also model and analyse the problem of overlapping spectrum band of the IoT groups in the industrial, scientific, and medical (ISM) band, aiming to achieve better packet transmission performance by adjusting the frequency set size.

The Channel Fading Influence of the Receiver Operating Characteristics of the TT&C Receiver Based on the Dual-Sequence Frequency Hopping

Aimed at the antijamming needs of the space tracking, telemetry command (TT&C) receiver under a low signal-to-noise ratio, the anti-interference advantage of the dual-sequence frequency hopping (DSFH) communication system is applied. The channel amplitude fading influence of the receiver operating characteristics (ROC) of the TT&C receiver based on the DSFH is studied. Firstly, based on the typical channel model of the Rayleigh fading without direct path transmission, the conditional Fokker-Planck equation (FPE) is obtained by analyzing the statistical independence of the Rayleigh fading signal and SR output particle moments. Secondly, the probability density function (PDF) of the DSFH signal via channel Rayleigh fading enhanced by stochastic resonance (SR) is obtained by introducing the decision time. Thirdly, the detection probability, false alarm probability, ROC, and system bit error rate (BER) of the DSFH signals enhanced by SR under the Rayleigh fading conditions are obtained, under the minimum BER criterion. Finally, the conclusions are reached: one is that the DSFH signals via channel Rayleigh fading can still be detected by the SR system under low SNR, and the other one is that the SNR can reach the -13 dB by the reception of DSFH signal enhanced by SR, when the Rayleigh fading parameter is 0.2042.

The research of interference recognition method in multi-nodes cooperative frequency-hopping communication based on time–frequency image analysis and deep learning

Interference recognition has a significant and irreplaceable importance in keeping battlefield communication reliability and securing information safety. However, the existing recognition algorithms have a common disadvantage, that the model scale is normally very huge. In order to satisfy the demand of decreasing mobile device weight and limiting the memory space, this article designed a lightweight neural network based on the idea of structure reparameterization. This network will take the time–frequency image of the interference signal as the input of the convolutional neural network (CNN), and import the efficient channel attention module, efficiently extract the feature of the interference signals, thus benefiting the interference recognition. The simulation experiment indicates that the network has a high accuracy rate in interference recognition in the circumstances when the quantity of parameters is not huge. At the same time, in order to solve the problem of low recognition accuracy due to the limit of information, this article designed an interference recognition algorithm based on multi-nodes collaboration technology. Considering jamming-to-noise ratio (JNR) could differ for each node, this article set a node select module, that could select the nodes that contain a higher result of JNR as the input of the fusion algorithm when each JNR is highly different, which could adjust abnormal influence from each node. The simulation experiment indicates that, the interference recognition algorithm based on multi-nodes collaboration technology could efficiently increase interference recognize accuracy, and reduce the influence from low JNR nodes.

Variable-Speed Frequency-Hopping Signal Sorting: Spectrogram Is Sufficient

In this paper, we present a novel signal sorting method aimed at reducing the impact of interference and noise while achieving blind detection and accurate sorting of a variable-speed frequency-hopping communication system. To achieve this, we combine spectrogram analysis with an innovative sorting approach. First, we generate the spectrogram of the received signal, and then employ a morphology filter to effectively eliminate noise and sweep frequency interference from the spectrogram. Subsequently, we identify and mark connected domains in the spectrogram, from which we extract the duration data to create a dataset specifically for separating fixed-frequency interference. Furthermore, we propose a specialized time alignment algorithm designed to accommodate the unique characteristics of variable-speed frequency-hopping signals, enabling precise sorting of variable-speed frequency-hopping signals. Through rigorous comparative evaluations against existing algorithms, we demonstrate that our proposed approach provides superior accuracy by offering a clearer representation of the time–frequency situation of the received signals. The proposed method provides a high correct sorting probability which is equal to 0.8 when signal-to-noise ratio is 0 dB and reaches 1 when signal-to-noise ratio reaches over 12 dB. In comparison, the correct sorting probability of the comparison algorithm is far inferior to the proposed algorithm.

RETRACTED ARTICLE: Enhancing throughput using channel access priorities in frequency hopping network using federated learning

The data are sent by the nodes taking part in frequency hopping communications (FHC) utilising carrier frequencies and time slots that are pseudo-randomly assigned. Because of this, a high degree of protection against eavesdropping and anti-interference capabilities is provided. When using FHC in an environment, sharing time and frequency resources, avoiding collisions, and differentiating services are all made more complex as a result of this. A protocol for FHC that is based on dispersed wireless networks is presented by the authors of this research. It is a mechanism for multiple access control, which is prioritised and distributed. The ratio of empty channels metric can be found in the previous sentence. It is possible to provide priority in channel access by assigning different preset ratios of empty channel thresholds to the various traffic classes. Frames from frequency spread segments that have a partial collision are included as well. An analytical model is simulated for the analysis in terms of collision probability, transmission probability, and frame service time in order to carry out a theoretical examination of the performance of FHC. The objective of this inquiry is to determine how well FHC works. The analytical model has been proven correct by the exhaustive simulations as well as the theoretical findings. Cloud platforms are often used in the instruction of the most cutting-edge machine learning techniques of today, such as deep neural networks. This is done in order to take advantage of the cloud's capacity to scale elastically. In order to satisfy the criteria of these sorts of applications, federated learning, has been proposed as a distributed machine learning solution. This is done in order to fulfil the requirements of these kinds of applications. In federated learning (FL), even though everyone who uses the system works together to train a model, nobody ever shares their data with anybody else. Each user trains a local model with their own data, and then communicates the updated models with a FL server so that the data can be aggregated and a global model can be constructed. This process ensures that each user's model is unique. This process is repeated until a global model has been developed. This kind of training not only reduces the amount of network overhead that is necessary to transfer data to a centralised server, but it also safeguards the personal information of the users. Within the framework of this work, we looked at the feasibility of using the FL technique of learning on the many devices that are part of the dispersed network. On a centralised server, we conduct an analysis of the performance of the FL model by comparing its accuracy and the amount of time it takes to train using a range of various parameter value combinations. Additionally, the accuracy of these federated models may be made to reach a level that is comparable to that of the accuracy of central models.

Intelligent Reception of Frequency Hopping Signals Based on CVDP

The frequency hopping communication systems have been widely used in anti-jamming communication due to their anti-interception and anti-interference capabilities. With the increasingly complex electromagnetic environment, the transmitter of the frequency hopping communication system adopts more flexible frequency hopping patterns to cope with interference. Therefore, to address the problem of traditional frequency hopping receiver systems being unable to receive properly due to intelligent frequency hopping sequence decisions made at the transmitter, we design a CVDP (CNN_VIT_Dual Multi-head Probsparse Self-attention) network based on a convolutional neural network (CNN) module and Transformer Encoder module to realize the intelligent reception of frequency hopping signals. The designed CNN module extracts the global features and spatial information of the time-frequency spectrograms and serves as the input of the VIT (Vision Transformer) network, which not only solves the problem that the VIT network is difficult to converge on small data sets but also improves the stability and robustness of the VIT network. In the Transformer Encoder module of the VIT network, a dual multi-head self-attention mechanism is used to extract critical time-frequency features and a Probsparse self-attention idea is introduced to reduce the computational complexity. Simulation results on hopping frequency estimation and received BER(Bit Error Rate) of CVDP networks in different interference and channel environments show that when a hopping sequence is unknown, CVDP-based intelligent hopping receiver systems can achieve nearly the same receiver performance as conventional reception systems in which a hopping sequence is known.

Mathematical modeling of underwater signal anomaly perception based on multi-sensor data fusion

There is a lot of interference in underwater environment, which can affect underwater signal transmission. For this reason, an abnormal perception model of underwater signals based on multi-sensor data fusion is proposed. The underwater frequency hopping communication method is used to select the channel and construct the mathematical model of underwater communication signal. A signal modulation and recognition system for multi-sensor reception is established, and the multi-sensor data fusion model is obtained by calculating the support margin. The energy spectrum distribution is obtained by decomposition of abnormal features with wavelet detector in time domain. The deep learning algorithm is used to reorganize the signal with the minimum detection error, and the mathematical model of underwater signal anomaly perception is obtained. The experimental results show that the average accuracy of the method for locating abnormal underwater communication signals is 95.5%, the misrecognition rate of abnormal underwater signals is less than 2%, and the time-consuming for sensing abnormal signals of 420 MB underwater communication is less than 3 s.

Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications

The frequency-hopping communication system has been widely used in anti-jamming communication due to its anti-interception and anti-jamming performance. With the increasingly complex electromagnetic environment, the frequency-hopping communication system needs more flexible frequency-hopping patterns to deal with interferences, which brings great challenges to the communication receiver. In this paper, an intelligent receiving scheme of frequency-hopping sequences is proposed, which combines time–frequency analysis with deep learning to realize an intelligent estimation of frequency-hopping sequences. A hybrid network module is designed by combining a convolutional neural network (CNN) with a gated recurrent unit (GRU). In the proposed network module, the combination of a residual network (ResNet) and squeeze and extraction (SE) improves the feature extraction and expression capabilities of the CNN network. The GRU network is proposed to solve the problem of dealing with signals with variant input lengths. A transfer learning scheme is further proposed to deal with communications systems with different frequency-hopping sets. Simulation results show that the proposed method has strong generalization ability and robustness, and the bit error rate (BER) performance of intelligent receiving is close to the receiving performance under ideal conditions.

Optimal Constructions of Low-Hit-Zone Frequency-Hopping Sequence Sets via Cyclotomy

In complex environments of wireless communication, to improve the communication anti-interference performance, quasi-synchronous frequency-hopping (FH) communication system requires low-hit-zone (LHZ) FH sequence sets with optimal Hamming correlation (HC) properties and flexible parameters. In this paper, based upon cyclotomy theory, two classes of LHZ FH sequence sets with flexible parameters not included in the literatures are designed, and the periodic HC properties of which are analyzed. It turns out that the designed LHZ FH sequence sets are optimal on the maximum periodic HC. And thus the new constructions can be used to provide optimal FH sequence sets for quasi-synchronous FH communication system.

Optimal and near-optimal frequency-hopping sequences based on Gaussian period

<abstract><p>Frequency-hopping sequences (FHSs) have a decisive influence on the whole frequency-hopping communication system. The Hamming correlation function plays an important role in evaluating the performance of FHSs. Constructing FHS sets that meet the theoretical bounds is crucial for the research and development of frequency-hopping communication systems. In this paper, three new classes of optimal FHSs based on trace functions are constructed. Two of them are optimal FHSs and the corresponding periodic Hamming autocorrelation value is calculated by using the known Gaussian period. It is shown that the new FHSs are optimal according to the Lempel-Greenberger bound. The third class of FHSs is the near-optimal FHSs.</p></abstract>

Joint power and hopping rate adaption against follower jammer based on deep reinforcement learning

AbstractAs an effective anti‐jamming approach, frequency‐hopping (FH) technology has been widely applied to tactical communication system, providing reliable communication guarantee and improving resilience against conventional interference under strong confrontation environment. Key challenges for tactical wireless communication network face are the smart follower jammer with responsive spectrum reconnaissance and intelligent decision‐making capabilities. In response, this article investigates a deep reinforcement learning based anti‐jamming scheme, with the aim of maximizing the system throughput. The interactions between a radio transmitter and a smart follower jammer are formulated as a hierarchical anti‐jamming dynamic game model, in which the radio terminal decides transmission power and hopping rate according to the state feedback information, and the jammer chooses spectrum scanning rate accordingly to minimizing the rewards of the FH communication system. We prove that there is a Nash equilibrium (NE) strategy for static and dynamic environment in the game. A double deep Q‐network with prioritized experience reply (PDDQN) based anti‐jamming scheme is proposed to approximating the optimal power control and hopping strategy without being aware of the environment and jamming parameters. Finally, simulation results demonstrate that the proposed algorithm efficiently provide better throughput and jamming resistance.

A fast TDOA and VDOA estimation method of the wideband frequency hopping signal

In the mobile multi-station passive positioning scenario, the carrier frequency hopping of the wideband frequency hopping communication signal will produce a changing Doppler frequency shift. Therefore, it is not possible to directly estimate the Doppler frequency difference of the observed signal containing multiple pulses. In this case, only the time difference and frequency difference of the single-hop signal can be estimated, but the accuracy is low. The existing methods often need the carrier frequency of each FH pulse signal as a priori information, and the high computational complexity of the two-dimensional search method of the time difference and frequency difference is difficult to overcome. Aiming at the above problems, a fast and high-precision estimation algorithm for the time difference of arrival and velocity difference of wideband frequency hopping signals is proposed in this paper. Firstly, the wideband frequency hopping signal is segmented in the time domain with multiple pulses as the unit, the signal model of time-varying time difference is established, and the time difference of each short-time segment is estimated by the generalized cross-correlation algorithm. The estimated values of the initial time difference and Doppler velocity difference can be worked out by least squares fitting of the time difference sequence obtained from the single pulse estimation. Finally, taking the Link16 data link signal as an example, the simulation experiment of the proposed method is conducted, which verifies the correctness and performance superiority of the algorithm.

Time-frequency analysis-based deep interference classification for frequency hopping system

It is known that interference classification plays an important role in protecting the authorized communication system and avoiding its performance degradation in the hostile environment. In this paper, the interference classification problem for the frequency hopping communication system is discussed. Considering the possibility of the presence of multiple interferences in the frequency hopping system, in order to fully extract effective features of the interferences from the received signals, the linear and bilinear transform-based composite time-frequency analysis method is adopted. Then, the time-frequency spectrograms obtained from the time-frequency analysis are constructed as matching pairs and input to the deep neural network for classification. In particular, the Siamese neural network is used as the classifier, where the paired spectrograms are input into the two sub-networks of the deep networks, and these two sub-networks extract the features of the paired spectrograms for interference-type classification. The simulation results confirm that the proposed algorithm can obtain higher classification accuracy than both traditional single time-frequency representation-based approach and the AlexNet transfer learning or convolutional neural network-based methods.

Chinese remainder theorem‐based construction of optimal no‐hit‐zone frequency sequence set with wide gap

In frequency-hopping (FH) communication systems, frequency-hopping sequences (FHSs) determine systems' performance. For the quasi-synchronous FH communication systems, no-hit-zone (NHZ) frequency sequences are good at preventing multi-path interferences. If the two neighbouring frequency components of an FHS have enough gap, the FH communication system's anti-interference ability will be significantly improved. Here, a Chinese remainder theorem-based construction of optimal NHZ-FHS sets with wide gap is proposed.