Adversarial attacks and defenses for digital communication signals identification

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management systems.AI has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and industry.However,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial attacks.Adversarial examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong results.Therefore,the security of AI models for the digital communication signals identification is the premise of its efficient and credible applications.In this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial principle.Next we present more detailed adversarial indicators to evaluate attack and defense behavior.Finally,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

Astrocyte-neuron communication mediated by the Notch signaling pathway:focusing on glutamate transport and synaptic plasticity

Maintaining glutamate homeostasis after hypoxic ischemia is important for synaptic function and neural cell activity,and regulation of glutamate transport between astrocyte and neuron is one of the important modalities for reducing glutamate accumulation.However,further research is needed to investigate the dynamic changes in and molecular mechanisms of glutamate transport and the effects of glutamate transport on synapses.The aim of this study was to investigate the regulatory mechanisms underlying Notch pathway mediation of glutamate transport and synaptic plasticity.In this study,Yorkshire neonatal pigs(male,age 3 days,weight 1.0–1.5 kg,n=48)were randomly divided into control(sham surgery group)and five hypoxic ischemia subgroups,according to different recovery time,which were then further subdivided into subgroups treated with dimethyl sulfoxide or a Notch pathway inhibitor(N-[N-(3,5-difluorophenacetyl-l-alanyl)]-S-phenylglycine t-butyl ester).Once the model was established,immunohistochemistry,immunofluorescence staining,and western blot analyses of Notch pathway-related proteins,synaptophysin,and glutamate transporter were performed.Moreover,synapse microstructure was observed by transmission electron microscopy.At the early stage(6–12 hours after hypoxic ischemia)of hypoxic ischemic injury,expression of glutamate transporter excitatory amino acid transporter-2 and synaptophysin was downregulated,the number of synaptic vesicles was reduced,and synaptic swelling was observed;at 12–24 hours after hypoxic ischemia,the Notch pathway was activated,excitatory amino acid transporter-2 and synaptophysin expression was increased,and the number of synaptic vesicles was slightly increased.Excitatory amino acid transporter-2 and synaptophysin expression decreased after treatment with the Notch pathway inhibitor.This suggests that glutamate transport in astrocytes-neurons after hypoxic ischemic injury is regulated by the Notch pathway and affects vesicle release and synaptic plasticity through the expression of synaptophysin.

Open World Recognition of Communication Jamming Signals

To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)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 0d B,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.

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

In recent years,the need for a fast,efficient and a reliable wireless network has increased dramatically.Numerous 5G networks have already been tested while a few are in the early stages of deployment.In noncooperative communication scenarios,the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them.The recent advancements in both Machine Learning(ML)and Deep Learning(DL)models demand the development of effective modulation recognition models with self-learning capability.In this background,the current research article designs aDeep Learning enabled Intelligent Modulation Recognition of Communication Signal(DLIMR-CS)technique for next-generation networks.The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals.In addition,the fractal feature extraction process is appliedwith the help of the Sevcik Fractal Dimension(SFD)approach.Then,the extracted features are fed into the Deep Variational Autoencoder(DVAE)model for the classification of the modulated signals.In order to improve the classification performance of the DVAE model,the Tunicate Swarm Algorithm(TSA)is used to finetune the hyperparameters involved in DVAE model.A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model.The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.

Context-Based Intelligent Scheduling and Knowledge Push Algorithms for AR-Assist Communication Network Maintenance

Maintenance is an important aspect in the lifecycle of communication network devices.Prevalent problems in the maintenance of communication networks include inconvenient data carrying and sub-optimal scheduling of work orders,which significantly restrict the efficiency of maintenance work.Moreover,most maintenance systems are still based on cloud architectures that slow down data transfer.With a focus on the completion time,quality,and load balancing of maintenance work,we propose in this paper a learning-based virus evolutionary genetic algorithm with multiple quality-ofservice(QoS)constraints to implement intelligent scheduling in an edge network.The algorithm maintains the diversity of the population and improves the speed of convergence using a fitness function and a learning-based population generation mechanism.The test results demonstrate that the algorithm delivers good performance in terms of load balancing and QoS guarantee.We also propose a knowledge push algorithm based on a context model for intelligently pushing relevant knowledge according to the given conditions.The simulation results demonstrate that our scheme can improve the efficiency of on-site maintenance.

Quantum stochastic filters for nonlinear time-domain filtering of communication signals

Principles and performances of quantum stochastic filters are studied for nonlinear time-domain filtering of communication signals. Filtering is realized by combining neural networks with the nonlinear Schroedinger equation and the time-variant probability density function of signals is estimated by solution of the equation. It is shown that obviously different performances can be achieved by the control of weight coefficients of potential fields. Based on this characteristic, a novel filtering algorithm is proposed, and utilizing this algorithm, the nonlinear waveform distortion of output signals and the denoising capability of the filters can be compromised. This will make the application of quantum stochastic filters be greatly extended, such as in applying the filters to the processing of communication signals. The predominant performance of quantum stochastic filters is shown by simulation results.

Signal molecule-mediated hepatic cell communication during liver regeneration

Liver regeneration is a complex and well-orchestrated process,during which hepatic cells are activated to produce large signal molecules in response to liver injury or mass reduction.These signal molecules,in turn,set up the connections and cross-talk among liver cells to promote hepatic recovery.In this review,we endeavor to summarize the network of signal molecules that mediates hepatic cell communication in the regulation of liver regeneration.

Na^(+)/K^(+)-ATPase:ion pump,signal transducer,or cytoprotective protein,and novel biological functions

Na^(+)/K^(+)-ATPase is a transmembrane protein that has important roles in the maintenance of electrochemical gradients across cell membranes by transporting three Na^(+)out of and two K^(+)into cells.Additionally,Na^(+)/K^(+)-ATPase participates in Ca^(2+)-signaling transduction and neurotransmitter release by coordinating the ion concentration gradient across the cell membrane.Na^(+)/K^(+)-ATPase works synergistically with multiple ion channels in the cell membrane to form a dynamic network of ion homeostatic regulation and affects cellular communication by regulating chemical signals and the ion balance among different types of cells.Therefo re,it is not surprising that Na^(+)/K^(+)-ATPase dysfunction has emerged as a risk factor for a variety of neurological diseases.However,published studies have so far only elucidated the important roles of Na^(+)/K^(+)-ATPase dysfunction in disease development,and we are lacking detailed mechanisms to clarify how Na^(+)/K^(+)-ATPase affects cell function.Our recent studies revealed that membrane loss of Na^(+)/K^(+)-ATPase is a key mechanism in many neurological disorders,particularly stroke and Parkinson's disease.Stabilization of plasma membrane Na^(+)/K^(+)-ATPase with an antibody is a novel strategy to treat these diseases.For this reason,Na^(+)/K^(+)-ATPase acts not only as a simple ion pump but also as a sensor/regulator or cytoprotective protein,participating in signal transduction such as neuronal autophagy and apoptosis,and glial cell migration.Thus,the present review attempts to summarize the novel biological functions of Na^(+)/K^(+)-ATPase and Na^(+)/K^(+)-ATPase-related pathogenesis.The potential for novel strategies to treat Na^(+)/K^(+)-ATPase-related brain diseases will also be discussed.

Modulation recognition of communication signals based on SCHKS-SSVM

A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.

Predictive Tracking Implementation for Mobile Communication Using Programmable Metasurface

The programmable metasurface(PM)is an antenna array architecture that realizes flexible beam steering.This functionality is achieved by controlling the unit cells designed with micro components such as positive-intrinsic-negative(PIN)diodes,which offers potential cost reductions in the next generation wireless communication systems.Although PM has been a popular topic in antenna design,its implementations in real-time systems accompanied by signal processing algorithms are challenging.In this paper,novel predictive tracking algorithms for mobile communication scenarios using a PM are created and implemented in a real-time system operating at 28 GHz.An angular speed prediction(ASP)algorithm is proposed to compute the position of user equipment(UE)based on the previously recorded beam directions.As another solution,an angle correction(AC)algorithm is proposed to further improve the prediction and tracking accuracy.As a benchmark,the comparisons to a previous PM tracking algorithm without prediction are presented.Both simulation and measurement results show that the prediction algorithms successfully improve the tracking performance,which also prove the feasibilities of PM-based systems to solve complex real-time signal processing problems.

BER Performance of FSO Communication System with Differential Signaling over Correlated Atmospheric Turbulence Fading

Free space optical(FSO) communication system with differential signaling possesses the advantage of requiring no channel state information and avoiding computational load or link throughput reduction compared to the systems with conventional receivers. In this work, we investigate bit error rate(BER) performance of this system over partially and fully correlated atmospheric turbulence fading. In order to conduct the above analysis, we obtain a probability density functions(PDF) of the channel fading on the differential signals and derive our instantaneous BER using differential signaling scheme. Based on these results, we develop two closed-form mathematical expressions for the average BER under fully correlated and partially correlated fading in the convergent infinite series confirmed by Cauchy’s ratio test. The accuracy of the derived BER expressions is demonstrated by the Monte Carlo simulations, and the analyses for the effects of the system parameters on the BER performance are provided.

Extraction of periodic signals in chaotic secure communication using Duffing oscillators

This paper presents a novel approach to extract the periodic signals masked by a chaotic carrier. It verifies that the driven Duffing oscillator is immune to the chaotic carrier and sensitive to certain periodic signals. A preliminary detection scenario illustrates that the frequency and amplitude of the hidden sine wave signal can be extracted from the chaotic carrier by numerical simulation. The obtained results indicate that the hidden messages in chaotic secure communication can be eavesdropped utilizing Duffing oscillators.

Analysis of reflected signal of quad rotor UAV based on model fitting in mobile communication system

In view of the many scenes of unmanned aerial vehicle(UAV)detection,a third-party signal source is used to design a receiver to monitor the UAV.It is of great significance to understand the reflection of the signal illuminating the UAV.Taking the communication base station(BS)signal as the third-party signal source,and considering the complete transmission link,reflection changes and loss fading of the communication signal,this study conducts model fitting for irregular UAV targets,simplifying complex targets into a combination of simple targets.Furthermore,the influence of the dielectric constant of the target surface and the signal irradiation angle on the signal reflection is analyzed.The analysis shows that the simulation results of this model fitting method are consistent with the results of other literature,which provides theoretical support for the detection of low and slow small targets such as UAVs.

High-Sensitivity Acquisition of Ultrahigh Dynamic Direct Sequence Spread Spectrum Signals in Space Communications

According to the requirements of the high-sensitivity acquisition of Direct Sequence Spread Spectrum(DSSS) signals under ultrahigh dynamic environments in space communications, a three-dimensional joint search of the phase of Pseudo-Noise-code(PN-code),Doppler frequency and its rate-of-change is presented to achieve high sensitivity in sensing high-frequency dynamics. By eliminating the correlation peak loss caused by ultrahigh Doppler frequency and its rate-of-change offset,the proposed method improves the acquisition sensitivity by increasing the non-coherent accumulation time. The validity of the algorithm is proved by theoretical analysis and simulation results. It is shown that signals with a carrier- to-noise ratio as low as 39 dBHz can be captured with high performance when the Doppler frequency is up to ±1 MHz and its rate-of-change is up to ±200 kHz/s.

Dual-Use Signal Design for Radar and Communication via Joint Orthogonal Signal and Phase Modulation

This paper proposes a new information modulation resorting to orthogonal signal and its phase for dual-function radar communication(DFRC)systems.Focusing on the standardized linear frequency modulation(LFM)signal by additional phase,a bank of signals enjoying satisfactory autocorrelation and cross-correlation characteristics,are generated.Then,these signals map the different information as well as their phases are also modulated to increase the communication bit rate,thus yielding a series of dual-use signals.Finally,the radar detection and communication performance of dual-use signals are also provided through numerical simulation and half-physical platform verification,confirming the effectiveness of the designed signals compared with the existing design strategy.

Sounding the alarm:Functionally referential signaling in Azure-winged Magpie

Functionally referential signals are a complex form of communication that conveys information about the external environment.Such signals have been found in a range of mammal and bird species and have helped us understand the complexities of animal communication.Corvids are well known for their extraordinary cognitive abilities,but relatively little attention has been paid to their vocal function.Here,we investigated the functionally referential signals of a cooperatively breeding corvid species,Azure-winged Magpie(Cyanopica cyanus).Through field observations,we suggest that Azure-winged Magpie uses referential alarm calls to distinguish two types of threats:’rasp’ calls for terrestrial threats and ’chatter’ calls for aerial threats.A playback experiment revealed that Azure-winged Magpies responded to the two call types with qualitatively different behaviors.They sought cover by flying into the bushes in response to the ’chatter’ calls,and flew to or stayed at higher positions in response to ’rasp’ calls,displaying a shorter response time to ’chatter’ calls.Significant differences in acoustic structure were found between the two types of calls.Given the extensive cognitive abilities of corvids and the fact that referential signals were once thought to be unique to primates,these findings are important for expanding our understanding of social communication and language evolution.

Iterative Signal Detection Based Soft Interference Cancellation for Satellite-Terrestrial Downlink NOMA Systems

To improve the bit error rate(BER)performance of multi-user signal detection in satelliteterrestrial downlink non-orthogonal multiple access(NOMA)systems,an iterative signal detection algorithm based on soft interference cancellation with optimal power allocation is proposed.Given that power allocation has a significant impact on BER performance,the optimal power allocation is obtained by minimizing the average BER of NOMA users.According to the allocated powers,successive interference cancellation(SIC)between NOMA users is performed in descending power order.For each user,an iterative soft interference cancellation is performed,and soft symbol probabilities are calculated for soft decision.To improve detection accuracy and without increasing the complexity,the aforementioned algorithm is optimized by adding minimum mean square error(MMSE)signal estimation before detection,and in each iteration soft symbol probabilities are utilized for soft-decision of the current user and also for the update of soft interference of the previous user.Simulation results illustrate that the optimized algorithm i.e.MMSE-IDBSIC significantly outperforms joint multi-user detection and SIC detection by 7.57dB and 8.03dB in terms of BER performance.

For LEO Satellite Networks: Intelligent Interference Sensing and Signal Reconstruction Based on Blind Separation Technology

In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.

Emotion Measurement Using Biometric Signal

In recent years, research on the estimation of human emotions has been active, and its application is expected in various fields. Biological reactions, such as electroencephalography (EEG) and root mean square successive difference (RMSSD), are indicators that are less influenced by individual arbitrariness. The present study used EEG and RMSSD signals to assess the emotions aroused by emotion-stimulating images in order to investigate whether various emotions are associated with characteristic biometric signal fluctuations. The participants underwent EEG and RMSSD while viewing emotionally stimulating images and answering the questionnaires. The emotions aroused by emotionally stimulating images were assessed by measuring the EEG signals and RMSSD values to determine whether different emotions are associated with characteristic biometric signal variations. Real-time emotion analysis software was used to identify the evoked emotions by describing them in the Circumplex Model of Affect based on the EEG signals and RMSSD values. Emotions other than happiness did not follow the Circumplex Model of Affect in this study. However, ventral attentional activity may have increased the RMSSD value for disgust as the β/θ value increased in right-sided brain waves. Therefore, the right-sided brain wave results are necessary when measuring disgust. Happiness can be assessed easily using the Circumplex Model of Affect for positive scene analysis. Improving the current analysis methods may facilitate the investigation of face-to-face communication in the future using biometric signals.