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.

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.

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.

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.

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.

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.

FUNDAMENTAL COMMUNICATIONS THEORIES AND SIGNAL PROCESSING TECHNIQUES FOR AMORPHOUS CELLULAR SYSTEMS

The rapid developing of the fourth generation(4G)wireless communications has aroused tremendous demands for high speed data transmission due to the dissemination of various types of the intelligent user terminals as well as the wireless multi-media services.It is predicted that the network throughput will increase

Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,etc.It becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex platform.Several works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so on.In addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal classification.In this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated signals.The proposed CSM-FFDNN model involves two major processes namely FE and classification.The proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated signals.Besides,the extracted features are fed into the DNN model for modulation signal classification.To improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN model.The experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.

A widespread response of Gram-negative bacterial acyl-homoserine lactone receptors to Gram-positive Streptomyces γ-butyrolactone signaling molecules

Cell-cell communication is critical for bacterial survival in natural habitats,in which miscellaneous regulatory networks are encompassed.However,elucidating the interaction networks of a microbial community has been hindered by the population complexity.This study reveals thatγ-butyrolactone(GBL)molecules from Streptomyces species,the major antibiotic producers,can directly bind to the acyl-homoserine lactone(AHL)receptor of Chromobacterium violaceum and influence violacein production controlled by the quorum sensing(QS)system.Subsequently,the widespread responses of more Gram-negative bacterial AHL receptors to Gram-positive Streptomyces signaling molecules are unveiled.Based on the cross-talk between GBL and AHL signaling systems,combinatorial regulatory circuits(CRC)are designed and proved to be workable in Escherichia coli(E.coli).It is significant that the QS systems of Gram-positive and Gram-negative bacteria can be bridged via native Streptomyces signaling molecules.These findings pave a new path for unlocking the comprehensive cell-cell communications in microbial communities and facilitate the exploitation of innovative regulatory elements for synthetic biology.

20.231 Gbit/s tricolor red/green/blue laser diode based bidirectional signal remodulation visible-light communication system

We propose and experimentally demonstrate a recorded 1-m bidirectional 20.231-Gbit/s tricolor R/G/B laser diode(LD) based visible-light communication(VLC) system supporting signal remodulation. In the signal remodulation system, an LD source is not needed at the client side. The client reuses the downstream signal sent from the central office(CO) and remodulates it to produce the upstream signal. As the LD sources are located at the CO, the laser wavelength and temperature managements at the cost-sensitive client side are not needed.This is the first demonstration, to our knowledge, of a >20 Gbit∕s data rate tricolor R/G/B VLC signal transmission supporting upstream remodulation.

Signal processing in underwater acoustic communication system for manned deep submersible "Jiaolong"

For the first time in the world, underwater acoustic transmission of images, human voice, data and texts between vehicle under 7000 m depth and surface ship was accomplished by underwater acoustic communication system of manned deep submersible Jiaolong＇. In this paper, signal processing in underwater acoustic communication system for manned deep submersible ＂Jiaolong＂ is introduced. （1） Four communication methods are integrated to meet different needs： 1） coherent underwater acoustic communication, with a variable transmission rate from 5 kbps to 15 kbps, to transmit images. 2） Non-coherent underwater acoustic com- munication, with a transmission rate 300 bps, to transmit texts, instructions, and sensor data. 3） Spread spectrum underwater acoustic communication, with a transmission rate 16 bps, to transmit instructions. 4） Underwater voice communication, using single sideband modulation to transmit hmnan voice. （2） Signal processing method in coherent communication mainly consists of concatenation of decision feedback equalizer and Turbo decoder, and wavelet based image compression with fixed length coding. In the equalizer, Doppler compensation, multi- channel combining and equalizer coefficients updating are all using fast self-optimized adaptive algorithm. （3） A linear hydrophone array is lowered from the mother ship to certain depth, and spatial diversity combining technology is adopted. （4） Diving trials of ＂Jiaolong＂ were carried out in Pacific Ocean. The communication range can cover nearly all ocean depth. One optical/acoustic image can be transmitted in 7 or 14 seconds.

Signal processing for noncoherent underwater acoustic communication approaching channel capacity

Noncoherent underwater acoustic communication channel in adverse conditions is modeled as a phase-random Rayleigh fading channel,and its capacity curve is derived.To approach the channel capacity curve,the concatenated code of the nonbinary LDPC code and the constant weight code is proposed for noncoherent communication which can late be iteratively decoded in the probability domain.Without information of channel amplitude or phase in the receiver,statistic parameters of the respective signal and noise bins were estimated based on the moment estimation method,the posterior probabilities of the constant weight code words were further calculated,and the nonbinary LDPC code was decoded with the nonbinary factor graph algorithm.It is verified by simulations that by utilizing the proposed concatenated code and its processing algorithm,gap to channel capacity curve is reduced by 3 dB when compared to the existing method.Underwater communication experiments were carried out in both deep ocean（vertical communication,5 km）and shallow lake（horizontal communication,near 3 km,delay spread larger than 50 ms）,in which the signal frequency band was 6 kHz to10 kHz,and the data transmission rate Was 357 bps.The proposed scheme can work properly in both experiments with a signal-to-noise ratio threshold of 2 dB.The performance of the proposed algorithm Was well verified by the experiments.

Passive location using CDMA mobile communication system based on repeater equipment

To overcome the drawback of a short operation range and low-resolution of a passive location system using a civil communication signal, the new idea that utilizes code division multiple access （CDMA） signal and repeater is disposed off. First, the CDMA passive location model and observation function are given, and the error source and error range are analyzed. Subsequently, the CDMA passive location algorithm in a repeater environment is described and simulated. The simulation result shows that the algorithm can provide the location value with high accuracy.

4th National Conference on Speech,Image,Communication,and Signal Processing,held in Beijing,25—27 October 1989

The 4th National Conference on Speech,Image,Communication and Signal Pro-cessing,which was sponsored by the Institute of Speech,Hearing,and Music Acoustics,Acoustical Society of China and the Institute of Signal Processing,Electronic Society ofChina,was held,25—27 October,1989,at Beijing Institute of Post and Telecommun-ication.The conference drew a registration of 150 from different places in the country,which made it the largest conference in the last eight years.The president of Institute of Speech,Hearing,and Music Acoustics,ASC,professorZHANG Jialu made a openning speech at the openning session,and the honorary presi-dent of Acoustical Society of China,professor MAA Dah-You and the president of