Cutting-Edge Challenges in Communication Technology and Computer Network Security

The rapid development of communication technology and computer networks has brought a lot of convenience to production and life,but it also increases the security problem.Information security has become one of the severe challenges faced by people in the digital age.Currently,the security problems facing the field of communication technology and computer networks in China mainly include the evolution of offensive technology,the risk of large-scale data transmission,the potential vulnerabilities introduced by emerging technology,and the dilemma of user identity verification.This paper analyzes the frontier challenges of communication technology and computer network security,and puts forward corresponding solutions,hoping to provide ideas for coping with the security challenges of communication technology and computer networks.

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

Mega-Constellations Based TT&C Resource Sharing: Keep Reliable Aeronautical Communication in an Emergency

With the development of the transportation industry, the effective guidance of aircraft in an emergency to prevent catastrophic accidents remains one of the top safety concerns. Undoubtedly, operational status data of the aircraft play an important role in the judgment and command of the Operational Control Center(OCC). However, how to transmit various operational status data from abnormal aircraft back to the OCC in an emergency is still an open problem. In this paper, we propose a novel Telemetry, Tracking,and Command(TT&C) architecture named Collaborative TT&C(CoTT&C) based on mega-constellation to solve such a problem. CoTT&C allows each satellite to help the abnormal aircraft by sharing TT&C resources when needed, realizing real-time and reliable aeronautical communication in an emergency. Specifically, we design a dynamic resource sharing mechanism for CoTT&C and model the mechanism as a single-leader-multi-follower Stackelberg game. Further, we give an unique Nash Equilibrium(NE) of the game as a closed form. Simulation results demonstrate that the proposed resource sharing mechanism is effective, incentive compatible, fair, and reciprocal. We hope that our findings can shed some light for future research on aeronautical communications in an emergency.

Protocol-Based Non-Fragile State Estimation for Delayed Recurrent Neural Networks Subject to Replay Attacks

Dear Editor, This letter focuses on the protocol-based non-fragile state estimation problem for a class of recurrent neural networks(RNNs). With the development of communication technology, the networked systems have received particular attentions. The networked system brings advantages such as easy to implement.

Covert LEO Satellite Communication Aided by Generative Adversarial Network Based Cooperative UAV Jamming

In this paper,we study the covert performance of the downlink low earth orbit(LEO)satellite communication,where the unmanned aerial vehicle(UAV)is employed as a cooperative jammer.To maximize the covert rate of the LEO satellite transmission,a multi-objective problem is formulated to jointly optimize the UAV’s jamming power and trajectory.For practical consideration,we assume that the UAV can only have partial environmental information,and can’t know the detection threshold and exact location of the eavesdropper on the ground.To solve the multiobjective problem,we propose the data-driven generative adversarial network(DD-GAN)based method to optimize the power and trajectory of the UAV,in which the sample data is collected by using genetic algorithm(GA).Simulation results show that the jamming solution of UAV generated by DD-GAN can achieve an effective trade-off between covert rate and probability of detection errors when only limited prior information is obtained.

Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication constraints.These transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear dynamics.Through the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

Trust-based co-operative routing for secure communication in mobile ad hoc networks

The working of a Mobile Ad hoc NETwork(MANET)relies on the supportive cooperation among the network nodes.But due to its intrinsic features,a misbehaving node can easily lead to a routing disorder.This paper presents two trust-based routing schemes,namely Trust-based Self-Detection Routing(TSDR)and Trust-based Cooperative Routing(TCOR)designed with an Ad hoc On-demand Distance Vector(AODV)protocol.The proposed work covers a wide range of security challenges,including malicious node identification and prevention,accurate trust quantification,secure trust data sharing,and trusted route maintenance.This brings a prominent solution for mitigating misbehaving nodes and establishing efficient communication in MANET.It is empirically validated based on a performance comparison with the current Evolutionary Self-Cooperative Trust(ESCT)scheme,Generalized Trust Model(GTM),and the conventional AODV protocol.The extensive simulations are conducted against three different varying network scenarios.The results affirm the improved values of eight popular performance metrics overcoming the existing routing schemes.Among the two proposed works,TCOR is more suitable for highly scalable networks;TSDR suits,however,the MANET application better with its small size.This work thus makes a significant contribution to the research community,in contrast to many previous works focusing solely on specific security aspects,and results in a trade-off in the expected values of evaluation parameters and asserts their efficiency.

Stackelberg Game for Wireless Powered and Backscattering Enabled Sensor Networks

This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point(AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two subproblems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation(SDR) and variable substitution techniques are applied to find a nearoptimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.

Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

This paper focuses on wireless-powered communication systems,which are increasingly relevant in the Internet of Things(IoT)due to their ability to extend the operational lifetime of devices with limited energy.The main contribution of the paper is a novel approach to minimize the secrecy outage probability(SOP)in these systems.Minimizing SOP is crucial for maintaining the confidentiality and integrity of data,especially in situations where the transmission of sensitive data is critical.Our proposed method harnesses the power of an improved biogeography-based optimization(IBBO)to effectively train a recurrent neural network(RNN).The proposed IBBO introduces an innovative migration model.The core advantage of IBBO lies in its adeptness at maintaining equilibrium between exploration and exploitation.This is accomplished by integrating tactics such as advancing towards a random habitat,adopting the crossover operator from genetic algorithms(GA),and utilizing the global best(Gbest)operator from particle swarm optimization(PSO)into the IBBO framework.The IBBO demonstrates its efficacy by enabling the RNN to optimize the system parameters,resulting in significant outage probability reduction.Through comprehensive simulations,we showcase the superiority of the IBBO-RNN over existing approaches,highlighting its capability to achieve remarkable gains in SOP minimization.This paper compares nine methods for predicting outage probability in wireless-powered communications.The IBBO-RNN achieved the highest accuracy rate of 98.92%,showing a significant performance improvement.In contrast,the standard RNN recorded lower accuracy rates of 91.27%.The IBBO-RNN maintains lower SOP values across the entire signal-to-noise ratio(SNR)spectrum tested,suggesting that the method is highly effective at optimizing system parameters for improved secrecy even at lower SNRs.

Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content.In light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of location.If 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of resources.Hence,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate prediction.The findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 s.Within a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G networks.These improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving congestion.By improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

A CSMA/CA based MAC protocol for hybrid Power-line/Visible-light communication networks:Design and analysis

Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware efficiency.Current research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)devices.So designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network CC.To solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 standards.Firstly,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,respectively.Based on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC MAC.In the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into consideration.Moreover,we prove the proposed analytical model has the solvability.Finally,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.

Sensing and Communication Integrated Fast Neighbor Discovery for UAV Networks

In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communication(ISAC),as an emerging technology in 6G mobile networks,has shown great potential in improving communication performance with the assistance of sensing information.ISAC obtains the prior information about node distribution,reducing the ND time.However,the prior information obtained through ISAC may be imperfect.Hence,an ND algorithm based on reinforcement learning is proposed.The learning automaton(LA)is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms.Besides,an efficient ND algorithm in the neighbor maintenance phase is designed,which applies the Kalman filter to predict node movement.Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32%compared with the Scan-Based Algorithm(SBA),which proves the efficiency of the proposed ND algorithms.

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

The current resource allocation in 5G vehicular networks for mobile cloud communication faces several challenges,such as low user utilization,unbalanced resource allocation,and extended adaptive allocation time.We propose an adaptive allocation algorithm for mobile cloud communication resources in 5G vehicular networks to address these issues.This study analyzes the components of the 5G vehicular network architecture to determine the performance of different components.It is ascertained that the communication modes in 5G vehicular networks for mobile cloud communication include in-band and out-of-band modes.Furthermore,this study analyzes the single-hop and multi-hop modes in mobile cloud communication and calculates the resource transmission rate and bandwidth in different communication modes.The study also determines the scenario of one-way and two-way vehicle lane cloud communication network connectivity,calculates the probability of vehicle network connectivity under different mobile cloud communication radii,and determines the amount of cloud communication resources required by vehicles in different lane scenarios.Based on the communication status of users in 5G vehicular networks,this study calculates the bandwidth and transmission rate of the allocated channels using Shannon’s formula.It determines the adaptive allocation of cloud communication resources,introduces an objective function to obtain the optimal solution after allocation,and completes the adaptive allocation process.The experimental results demonstrate that,with the application of the proposed method,the maximum utilization of user communication resources reaches approximately 99%.The balance coefficient curve approaches 1,and the allocation time remains under 2 s.This indicates that the proposed method has higher adaptive allocation efficiency.

Call for Papers--Feature Topic Vol. 22, No. 3, 2025 Special Issue of China Communications: Convergence of 6G empowered Edge Intelligence and Generative AI: Theories, Algorithms, and Applications

Generative artificial intelligence(AI), as an emerging paradigm in content generation, has demonstrated its great potentials in creating high-fidelity data including images, texts, and videos. Nowadays wireless networks and applications have been rapidly evolving from achieving “connected things” to embracing “connected intelligence”.

Multi-layer network embedding on scc-based network with motif

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based networks.In previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification.However,the content of semantic information is quite complex.Although graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of loss.Therefore,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network.The Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification.We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

Drivers for Inter-city Innovation Networks Across Chinese Cities:Modelling Physical Versus Intangible Effects

Cross-region innovation is widely recognized as an important source of the long-term regional innovation capacity.In the recent past,a growing number of studies has investigated the network structure and mechanisms of cross-region innovation collaboration in various contexts.However,existing research mainly focuses on physical effects,such as geographical distance and high-speed railway connections.These studies ignore the intangible drivers in a changing environment,the more digitalized economy and the increasingly solidified innovation network structure.Thus,the focus of this study is on estimating determinants of innovation networks,especially on intangible drivers,which have been largely neglected so far.Using city-level data of Chinese patents(excluding Hong Kong,Macao,and Taiwan Province of China),we trace innovation networks across Chinese cities over a long period of time.By integrating a measure on Information and Communications Technology(ICT)development gap and network structural effects into the general proximity framework,this paper explores the changing mechanisms of Chinese innovation networks from a new perspective.The results show that the structure of cross-region innovation networks has changed in China.As mechanisms behind this development,the results confirm the increasingly important role of intangible drivers in Chinese inter-city innovation collaboration when controlling for effects of physical proximity,such as geographical distance.Since digitalization and coordinated development are the mainstream trends in China and other developing countries,these countries'inter-city innovation collaboration patterns will witness dramatic changes under the influence of intangible drivers.

Robot-Oriented 6G Satellite-UAV Networks: Requirements, Paradigm Shifts, and Case Studies

Networked robots can perceive their surroundings, interact with each other or humans,and make decisions to accomplish specified tasks in remote/hazardous/complex environments. Satelliteunmanned aerial vehicle(UAV) networks can support such robots by providing on-demand communication services. However, under traditional open-loop communication paradigm, the network resources are usually divided into user-wise mostly-independent links,via ignoring the task-level dependency of robot collaboration. Thus, it is imperative to develop a new communication paradigm, taking into account the highlevel content and values behind, to facilitate multirobot operation. Inspired by Wiener’s Cybernetics theory, this article explores a closed-loop communication paradigm for the robot-oriented satellite-UAV network. This paradigm turns to handle group-wise structured links, so as to allocate resources in a taskoriented manner. It could also exploit the mobility of robots to liberate the network from full coverage,enabling new orchestration between network serving and positive mobility control of robots. Moreover,the integration of sensing, communications, computing and control would enlarge the benefit of this new paradigm. We present a case study for joint mobile edge computing(MEC) offloading and mobility control of robots, and finally outline potential challenges and open issues.

Deep Learning for Covert Communication

In recent years,deep learning has been gradually used in communication physical layer receivers and has achieved excellent performance.In this paper,we employ deep learning to establish covert communication systems,enabling the transmission of signals through high-power signals present in the prevailing environment while maintaining covertness,and propose a convolutional neural network(CNN)based model for covert communication receivers,namely Deep CCR.This model leverages CNN to execute the signal separation and recovery tasks commonly performed by traditional receivers.It enables the direct recovery of covert information from the received signal.The simulation results show that the proposed Deep CCR exhibits significant advantages in bit error rate(BER)compared to traditional receivers in the face of noise and multipath fading.We verify the covert performance of the covert method proposed in this paper using the maximum-minimum eigenvalue ratio-based method and the frequency domain entropy-based method.The results indicate that this method has excellent covert performance.We also evaluate the mutual influence between covert signals and opportunity signals,indicating that using opportunity signals as cover can cause certain performance losses to covert signals.When the interference-tosignal power ratio(ISR)is large,the impact of covert signals on opportunity signals is minimal.

Generative Adversarial Networks Based Digital Twin Channel Modeling for Intelligent Communication Networks

Integration of digital twin(DT)and wireless channel provides new solution of channel modeling and simulation,and can assist to design,optimize and evaluate intelligent wireless communication system and networks.With DT channel modeling,the generated channel data can be closer to realistic channel measurements without requiring a prior channel model,and amount of channel data can be significantly increased.Artificial intelligence(AI)based modeling approach shows outstanding performance to solve such problems.In this work,a channel modeling method based on generative adversarial networks is proposed for DT channel,which can generate identical statistical distribution with measured channel.Model validation is conducted by comparing DT channel characteristics with measurements,and results show that DT channel leads to fairly good agreement with measured channel.Finally,a link-layer simulation is implemented based on DT channel.It is found that the proposed DT channel model can be well used to conduct link-layer simulation and its performance is comparable to using measurement data.The observations and results can facilitate the development of DT channel modeling and provide new thoughts for DT channel applications,as well as improving the performance and reliability of intelligent communication networking.

Hybrid millimeter wave heterogeneous networks with spatially correlated user equipment

In this paper,we analyze a hybrid Heterogeneous Cellular Network(HCNet)framework by deploying millimeter Wave(mmWave)small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data rate.We consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations(SBSs)are deployed in the areas with high User Equipment(UE)density.Such user centric deployment of mmWave SBSs inevitably incurs correlation between UE and SBSs.For a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave communication.By using tools from stochastic geometry,we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power association.For UE clustering we considered Thomas cluster process and derive expressions for the association probability,coverage probability,area spectral efficiency,and energy efficiency.We also provide Monte Carlo simulation results to validate the accuracy of the derived expressions.Furthermore,we analyze the impact of mmWave operating frequency,antenna gain,small cell biasing,and BSs density to get useful engineering insights into the performance of hybrid mmWave HCNets.Our results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.