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.

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.

基于VMD与AdaBoost-SCN的海缆振动信号识别方法

海底光缆的在线监测和振动信号识别是保证其正常运行的关键技术。搭建了基于布里渊光时域分析系统,模拟不同工况下的海缆振动信号。针对海缆振动信号信息丰富、信噪比低,使用单一随机配置网络(stochastic configuration network,SCN)模型对信号识别准确率不高的问题,提出了自适应增强(adaptive boosting,AdaBoost)算法优化的随机配置网络(AdaBoost-SCN)识别方法。首先用变分模态分解(variational mode decomposition,VMD)算法分解海缆振动信号,构建特征向量;然后采用AdaBoost-SCN算法对振动信号分类。结果表明,所提方法有着很高的精度,并且具有很强的鲁棒性与泛化能力,提高了布里渊光时域分析系统振动信号识别的有效性。

A Survey on Underwater Acoustic Sensor Networks: Perspectives on Protocol Design for Signaling, MAC and Routing

Underwater acoustic sensor networks (UASNs) are often used for environmental and industrial sensing in undersea/ocean space, therefore, these networks are also named underwater wireless sensor networks (UWSNs). Underwater sensor networks are different from other sensor networks due to the acoustic channel used in their physical layer, thus we should discuss about the specific features of these underwater networks such as acoustic channel modeling and protocol design for different layers of open system interconnection (OSI) model. Each node of these networks as a sensor needs to exchange data with other nodes;however, complexity of the acoustic channel makes some challenges in practice, especially when we are designing the network protocols. Therefore based on the mentioned cases, we are going to review general issues of the design of an UASN in this paper. In this regard, we firstly describe the network architecture for a typical 3D UASN, then we review the characteristics of the acoustic channel and the corresponding challenges of it and finally, we discuss about the different layers e.g. MAC protocols, routing protocols, and signal processing for the application layer of UASNs.

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.

Communication-Censored Distributed Learning for Stochastic Configuration Networks

This paper aims to reduce the communication cost of the distributed learning algorithm for stochastic configuration networks (SCNs), in which information exchange between the learning agents is conducted only at a trigger time. For this purpose, we propose the communication-censored distributed learning algorithm for SCN, namely ADMMM-SCN-ET, by introducing the event-triggered communication mechanism to the alternating direction method of multipliers (ADMM). To avoid unnecessary information transmissions, each learning agent is equipped with a trigger function. Only if the event-trigger error exceeds a specified threshold and meets the trigger condition, the agent will transmit the variable information to its neighbors and update its state in time. The simulation results show that the proposed algorithm can effectively reduce the communication cost for training decentralized SCNs and save communication resources.

Efficient stochastic parallel gradient descent training for on-chip optical processor

In recent years,space-division multiplexing(SDM)technology,which involves transmitting data information on multiple parallel channels for efficient capacity scaling,has been widely used in fiber and free-space optical communication sys-tems.To enable flexible data management and cope with the mixing between different channels,the integrated reconfig-urable optical processor is used for optical switching and mitigating the channel crosstalk.However,efficient online train-ing becomes intricate and challenging,particularly when dealing with a significant number of channels.Here we use the stochastic parallel gradient descent(SPGD)algorithm to configure the integrated optical processor,which has less com-putation than the traditional gradient descent(GD)algorithm.We design and fabricate a 6×6 on-chip optical processor on silicon platform to implement optical switching and descrambling assisted by the online training with the SPDG algorithm.Moreover,we apply the on-chip processor configured by the SPGD algorithm to optical communications for optical switching and efficiently mitigating the channel crosstalk in SDM systems.In comparison with the traditional GD al-gorithm,it is found that the SPGD algorithm features better performance especially when the scale of matrix is large,which means it has the potential to optimize large-scale optical matrix computation acceleration chips.

自动交换光网络SCN性能和可靠性规划

自动交换光网络连接的恢复性能、可靠性依赖于信令通信网络 (SCN)的性能和可靠性 ,本文分析了SCN的网络性能、端对端时延特性和可靠性 ,得出影响自动交换光网络路由信令性能和可靠性的一些关键因素 ,提出了基于连接恢复性能的SCN规划方案和基于单链路失效无故障恢复的SCN和网络规划方案 ,性能规划方案可估计端对端连接恢复的时间范围 ,可靠性规划方案可解决单链路失效连接无故障恢复问题 ,并能提高路由广播效率 .

Signal averaging for noise reduction in anesthesia monitoring and control with communication channels

This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local area networks, or tele-medicine over a wide area network. Such systems involve communication channels which introduce noises due to quantization, channel noises, and have limited communication bandwidth resources. Usually signal averaging can be used effectively in reducing noise effects when remote monitoring and diagnosis are involved. However, when feedback is intended, we show that signal averaging will lose its utility substantially. To explain this phenomenon, we analyze stability margins under signal averaging and derive some optimal strategies for selecting window sizes. A typical case of anesthe-sia depth control problems is used in this development.

How functional foods play critical roles in human health

How do functional foods affect human health?To answer this question it is important to understand what happens when food is digested and taken up by the gastrointestinal(GI)tract.The gut is a selective nutrient absorption system and the most important signal transduction and information exchange system within the body.It acts as a signal transducer,a neuroendocrine sensor,and an immunological recognition and presentation system.It is also a complex information exchange system comprising a number of signaling networks involving GI cells and cells immobilized in organs or transported in blood.The bioactivity of functional foods in vivo may be due to their effects on such networks,but this raises the question of what signaling pathways are used by non-nutrients that cannot be absorbed by the gut.The purpose of this review is to describe intestinal nutrient transportation,identify a number of widely expressed receptors and signal transduction pathways,and outline our current understanding of the mechanisms involved in health and disease.At the end of the review,a method for developing a cell communication network is described.This network is convenient for investigating the effects of oral administration of experimental medicines,drugs,or functional foods on cytokines of interest.Because cytokines and chemokines are transported via the circulatory system,a simple 2–3 mL blood sample from a volunteer is a rich source of information.This method may become the gold standard for evaluating the effects of functional foods or medicines in vivo.©2012 Production and hosting by Elsevier B.V.on behalf of Beijing Academy of Food Sciences.

基于TSVD-SCN的光纤入侵信号识别算法研究

采用随机配置网络(SCN,Stochasti cconfiguration network)对光纤振动信号进行识别,常由于光纤预警系统的背景噪声问题使得网络的隐含层输出接近奇异,直接影响了SCN对光纤数据的识别准确率。因此本文提出了一种基于截断奇异值分解(Truncated singular value decomposition,TSVD)的SCN方法(TSVD-SCN)对光纤入侵信号进行识别。TSVD-SCN通过对网络的隐含层输出进行SVD分解并设置阈值去除其中较小的奇异值,以减少隐含层输出矩阵的条件数,提升网络识别率。本文利用占空比,平均幅差函数,FFT求能量占比的方法进行特征提取,采用基于TSVD-SCN算法对不同入侵振动特征矢量进行分类识别。实验证明,本文所提算法模型精度比SCN的模型精度更高,可以准确识别光纤入侵信号类型,对SCN网络在实际应用中对分类精度的提高有着重要意义。

Reliability of IEC 61850 based substation communication network architecture considering quality of repairs and common cause failures

Mission-critical IEC 61850 system architectures are designed to tolerate hardware failures to achieve the highest reliability performance.Hence,multi-channel systems are used in such systems within industrial facilities to isolate machinery when there are process abnormalities.Inevitably,multi-channel systems introduce Common Cause Failure(CCF)since the subsystems can rarely be independent.This paper integrates CCF into the Markov reliability model to enhance the model flexibility to investigate synchronous generator intra-bay SCN architecture reliability performance considering the quality of repairs and CCF.The Markov process enables integration of the impact of CCF factors on system performance.The case study results indicate that CCF,coupled with imperfect repairs,significantly reduce system reliability performance.High sensitivity is observed at low levels of CCF,whereas the highest level of impact occurs when the system diagnostic coverage is 99%based on ISO 13849-1,and reduces as the diagnostic coverage level reduces.Therefore,it is concluded that the severity of CCF depends more on system diagnostic coverage level than the repair efficiency,although both factors impact the system overall performance.Hence,CCF should be con-sidered in determining the reliability performance of mission-critical communication networks in power distribution centres.

Next Decade of Telecommunications Artificial Intelligence

It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computing industries,the rapid convergence of 5th generation mobile communication technology(5G)and AI is beginning to significantly transform the core communication infrastructure,network management,and vertical applications.The paper first outlined the individual roadmaps of mobile communications and AI in the early stage,with a concentration to review the era from 3rd generation mobile communication technology(3G)to 5G when AI and mobile communications started to converge.With regard to telecommunications AI,the progress of AI in the ecosystem of mobile communications was further introduced in detail,including network infrastructure,network operation and management,business operation and management,intelligent applications towards business supporting system(BSS)&operation supporting system(OSS)convergence,verticals and private networks,etc.Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations.Towards the next decade,the prospective roadmap of telecommunications AI was forecasted.In line with 3rd generation partnership project(3GPP)and International Telecommunication Union Radiocommunication Sector(ITU-R)timeline of 5G&6th generation mobile communication technology(6G),the network intelligence following 3GPP and open radio access network(O-RAN)routes,experience and intent-based network management and operation,network AI signaling system,intelligent middle-office based BSS,intelligent customer experience management and policy control driven by BSS&OSS convergence,evolution from service level agreement(SLA)to experience level agreement(ELA),and intelligent private network for verticals were further explored.The paper is concluded with the vision that AI will reshape the future beyond 5G(B5G)/6G landscape,and we need pivot our research and development(R&D),standardizations,and ecosystem to fully take the unprecedented opportunities.

Hopfield neural network-based image restoration with adaptive mixed-norm regularization

To overcome the shortcomings of traditional image restoration model and total variation image restoration model, we propose a novel Hopfield neural network-based image restoration algorithm with adaptive mixed-norm regularization. The new error function of image restoration combines the L2-norm and L1- norm regularization types. A method of calculating the adaptive scale control parameter is introduced. Experimental results demonstrate that the proposed algorithm is better than other algorithms with single norm regularization in the improvement of signal-to-noise ratio （ISNR） and vision effect.

Ultrahigh speed OOK-to-PSK conversion using linear filtering in silicon ring resonators

A scheme to achieve ultrahigh speed all-optical format conversion from on-off keying （OOK） to phase-shift keying （PSK） by using the linear filtering in the silicon ring resonators is proposed. It is shown that the OOK-to-PSK conversion can be achieved through a linear signal processing. Simulation results are provided for the 160-Gb/s non-return-to-zero （NRZ）-to-PSK and carrier-suppressed （CS） return-to-zero （RZ）-to-（CS）RZPSK conversions.