As an important mechanism in multi-agent interaction,communication can make agents form complex team relationships rather than constitute a simple set of multiple independent agents.However,the existing communication ...As an important mechanism in multi-agent interaction,communication can make agents form complex team relationships rather than constitute a simple set of multiple independent agents.However,the existing communication schemes can bring much timing redundancy and irrelevant messages,which seriously affects their practical application.To solve this problem,this paper proposes a targeted multiagent communication algorithm based on state control(SCTC).The SCTC uses a gating mechanism based on state control to reduce the timing redundancy of communication between agents and determines the interaction relationship between agents and the importance weight of a communication message through a series connection of hard-and self-attention mechanisms,realizing targeted communication message processing.In addition,by minimizing the difference between the fusion message generated from a real communication message of each agent and a fusion message generated from the buffered message,the correctness of the final action choice of the agent is ensured.Our evaluation using a challenging set of Star Craft II benchmarks indicates that the SCTC can significantly improve the learning performance and reduce the communication overhead between agents,thus ensuring better cooperation between agents.展开更多
Multi-agent technology has been used in many complex distributed and concurrent systems. A railway system is such a safety critical system and careful inves- tigation of the functional components is very important. St...Multi-agent technology has been used in many complex distributed and concurrent systems. A railway system is such a safety critical system and careful inves- tigation of the functional components is very important. Study of the various functional components in communi- cation-based train control （CBTC） system necessitates a good structural design followed by its validation and ver- ification through a formal modelling technique. The work presented here is the follow up of our multi-agent-based CBTC system for Indian railway designed using the methodology for engineering system of software agents. Behavioural analysis of the designed system involves several operating scenarios that arise during train run, and helps in understanding the reaction of the system to such situations. This validation and verification are very important as it allows the system designer to critically evaluate the desired function of the system and to correct the design errors, if any, before its actual implementation. Modelling, validation and verification of the structural design through Coloured petri net （CPN） are central to this paper. Analysis of simulation results validates the efficacy of the design.展开更多
This paper studies the problem of jamming decision-making for dynamic multiple communication links in wireless communication networks(WCNs).We propose a novel jamming channel allocation and power decision-making(JCAPD...This paper studies the problem of jamming decision-making for dynamic multiple communication links in wireless communication networks(WCNs).We propose a novel jamming channel allocation and power decision-making(JCAPD)approach based on multi-agent deep reinforcement learning(MADRL).In high-dynamic and multi-target aviation communication environments,the rapid changes in channels make it difficult for sensors to accurately capture instantaneous channel state information.This poses a challenge to make centralized jamming decisions with single-agent deep reinforcement learning(DRL)approaches.In response,we design a distributed multi-agent decision architecture(DMADA).We formulate multi-jammer resource allocation as a multiagent Markov decision process(MDP)and propose a fingerprint-based double deep Q-Network(FBDDQN)algorithm for solving it.Each jammer functions as an agent that interacts with the environment in this framework.Through the design of a reasonable reward and training mechanism,our approach enables jammers to achieve distributed cooperation,significantly improving the jamming success rate while considering jamming power cost,and reducing the transmission rate of links.Our experimental results show the FBDDQN algorithm is superior to the baseline methods.展开更多
Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so...Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present some sufficient conditions for the exponential stability of a particular category of switched systems.展开更多
Intelligent Reflecting Surface(IRS),with the potential capability to reconstruct the electromagnetic propagation environment,evolves a new IRSassisted covert communications paradigm to eliminate the negligible detecti...Intelligent Reflecting Surface(IRS),with the potential capability to reconstruct the electromagnetic propagation environment,evolves a new IRSassisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals leakage.However,when multiple IRSs are involved,accurate channel estimation is still a challenge due to the extra hardware complexity and communication overhead.Besides the crossinterference caused by massive reflecting paths,it is hard to obtain the close-formed solution for the optimization of covert communications.On this basis,the paper improves a heterogeneous multi-agent deep deterministic policy gradient(MADDPG)approach for the joint active and passive beamforming(Joint A&P BF)optimization without the channel estimation,where the base station(BS)and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency(CSE)cooperatively.Thanks to the‘centralized training and distributed execution’feature of MADDPG,each agent can execute the active or passive beamforming independently based on its partial observation without referring to others.Numeral results demonstrate that the proposed deep reinforcement learning(DRL)approach could not only obtain a preferable CSE of legitimate users and a low detection of probability(LPD)of warden,but also alleviate the communication overhead and simplify the IRSs deployment.展开更多
The cooperative control and stability analysis problems for the multi-agent system with sampled com- munication are investigated. Distributed state feedback controllers are adopted for the cooperation of networked age...The cooperative control and stability analysis problems for the multi-agent system with sampled com- munication are investigated. Distributed state feedback controllers are adopted for the cooperation of networked agents. A theorem in the form of linear matrix inequalities（LMI） is derived to analyze the system stability. An- other theorem in the form of optimization problem subject to LMI constraints is proposed to design the controller, and then the algorithm is presented. The simulation results verify the validity and the effectiveness of the pro- posed approach.展开更多
Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-...Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.展开更多
This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method...This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.展开更多
This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary obj...This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.展开更多
In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(...In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.展开更多
With the boom in maritime activities,the need for highly reliable maritime communication is becoming urgent,which is an important component of 5G/6G communication networks.However,the bandwidth reuse characteristic of...With the boom in maritime activities,the need for highly reliable maritime communication is becoming urgent,which is an important component of 5G/6G communication networks.However,the bandwidth reuse characteristic of 5G/6G networks will inevitably lead to severe interference,resulting in degradation in the communication performance of maritime users.In this paper,we propose a safe deep reinforcement learning based interference coordination scheme to jointly optimize the power control and bandwidth allocation in maritime communication systems,and exploit the quality-of-service requirements of users as the risk value references to evaluate the communication policies.In particular,this scheme designs a deep neural network to select the communication policies through the evaluation network and update the parameters using the target network,which improves the communication performance and speeds up the convergence rate.Moreover,the Nash equilibrium of the interference coordination game and the computational complexity of the proposed scheme are analyzed.Simulation and experimental results verify the performance gain of the proposed scheme compared with benchmarks.展开更多
The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport ...The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport industry must innovate in key technologies to ensure high-quality transmissions for passengers and railway operations.These systems must function effectively under high mobility conditions while prioritizing safety,ecofriendliness,comfort,transparency,predictability,and reliability.On the other hand,the proposal of 6 G wireless technology introduces new possibilities for innovation in communication technologies,which may truly realize the current vision of HSR.Therefore,this article gives a review of the current advanced 6 G wireless communication technologies for HSR,including random access and switching,channel estimation and beamforming,integrated sensing and communication,and edge computing.The main application scenarios of these technologies are reviewed,as well as their current research status and challenges,followed by an outlook on future development directions.展开更多
This paper presents a design method to implement an antenna array characterized by ultra-wide beam coverage,low profile,and low Sidelobe Level(SLL)for the application of Unmanned Aerial Vehicle(UAV)air-to-ground commu...This paper presents a design method to implement an antenna array characterized by ultra-wide beam coverage,low profile,and low Sidelobe Level(SLL)for the application of Unmanned Aerial Vehicle(UAV)air-to-ground communication.The array consists of ten broadside-radiating,ultrawide-beamwidth elements that are cascaded by a central-symmetry series-fed network with tapered currents following Dolph-Chebyshev distribution to provide low SLL.First,an innovative design of end-fire Huygens source antenna that is compatible with metal ground is presented.A low-profile,half-mode Microstrip Patch Antenna(MPA)is utilized to serve as the magnetic dipole and a monopole is utilized to serves as the electric dipole,constructing the compact,end-fire,grounded Huygens source antenna.Then,two opposite-oriented end-fire Huygens source antennas are seamlessly integrated into a single antenna element in the form of monopole-loaded MPA to accomplish the ultrawide,broadside-radiating beam.Particular consideration has been applied into the design of series-fed network as well as antenna element to compensate the adverse coupling effects between elements on the radiation performance.Experiment indicates an ultrawide Half-Power Beamwidth(HPBW)of 161°and a low SLL of-25 dB with a high gain of 12 d Bi under a single-layer configuration.The concurrent ultrawide beamwidth and low SLL make it particularly attractive for applications of UAV air-to-ground communication.展开更多
In the islanded operation of distribution networks,due to the mismatch of line impedance at the inverter output,conventional droop control leads to inaccurate power sharing according to capacity,resulting in voltage a...In the islanded operation of distribution networks,due to the mismatch of line impedance at the inverter output,conventional droop control leads to inaccurate power sharing according to capacity,resulting in voltage and frequency fluctuations under minor external disturbances.To address this issue,this paper introduces an enhanced scheme for power sharing and voltage-frequency control.First,to solve the power distribution problem,we propose an adaptive virtual impedance control based on multi-agent consensus,which allows for precise active and reactive power allocation without requiring feeder impedance knowledge.Moreover,a novel consensus-based voltage and frequency control is proposed to correct the voltage deviation inherent in droop control and virtual impedance methods.This strategy maintains voltage and frequency stability even during communication disruptions and enhances system robustness.Additionally,a small-signal model is established for system stability analysis,and the control parameters are optimized.Simulation results validate the effectiveness of the proposed control scheme.展开更多
The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defendin...The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defending against jamming and interference through spectrum allocation becomes challenging,especially when each UAV pair makes decisions independently.In this paper,we propose a cooperative multi-agent reinforcement learning(MARL)-based anti-jamming framework for I-UAVs,enabling UAV pairs to learn their own policies cooperatively.Specifically,we first model the problem as a modelfree multi-agent Markov decision process(MAMDP)to maximize the long-term expected system throughput.Then,for improving the exploration of the optimal policy,we resort to optimizing a MARL objective function with a mutual-information(MI)regularizer between states and actions,which can dynamically assign the probability for actions frequently used by the optimal policy.Next,through sharing their current channel selections and local learning experience(their soft Q-values),the UAV pairs can learn their own policies cooperatively relying on only preceding observed information and predicting others’actions.Our simulation results show that for both sweep jamming and Markov jamming patterns,the proposed scheme outperforms the benchmarkers in terms of throughput,convergence and stability for different numbers of jammers,channels and UAV pairs.展开更多
Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investig...Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investigate the integrated scheduling of communication,sensing,and control for UAV-aided FSO communication systems.Initially,a sensing-control model is established via the control theory.Moreover,an FSO communication channel model is established by considering the effects of atmospheric loss,atmospheric turbulence,geometrical loss,and angle-of-arrival fluctuation.Then,the relationship between the motion control of the UAV and radial displacement is obtained to link the control aspect and communication aspect.Assuming that the base station has instantaneous channel state information(CSI)or statistical CSI,the thresholds of the sensing-control pattern activation are designed,respectively.Finally,an integrated scheduling scheme for performing communication,sensing,and control is proposed.Numerical results indicate that,compared with conventional time-triggered scheme,the proposed integrated scheduling scheme obtains comparable communication and control performance,but reduces the sensing consumed power by 52.46%.展开更多
The accuracy of spot centroid positioning has a significant impact on the tracking accuracy of the system and the stability of the laser link construction.In satellite laser communication systems,the use of short-wave...The accuracy of spot centroid positioning has a significant impact on the tracking accuracy of the system and the stability of the laser link construction.In satellite laser communication systems,the use of short-wave infrared wavelengths as beacon light can reduce atmospheric absorption and signal attenuation.However,there are strong non-uniformity and blind pixels in the short-wave infrared image,which makes the image distorted and leads to the decrease of spot centroid positioning accuracy.Therefore,the high-precision localization of the spot centroid of the short-wave infrared images is of great research significance.A high-precision spot centroid positioning model for short-wave infrared is proposed to correct for non-uniformity and blind pixels in short-wave infrared images and quantify the localization errors caused by the two,further model-based localization error simulations are performed,and a novel spot centroid positioning payload for satellite laser communications has been designed using the latest 640×512 planar array InGaAs shortwave infrared detector.The experimental results show that the non-uniformity of the corrected image is reduced from 7%to 0.6%,the blind pixels rejection rate reaches 100%,the frame rate can be up to 2000 Hz,and the spot centroid localization accuracy is as high as 0.1 pixel point,which realizes high-precision spot centroid localization of high-frame-frequency short-wave infrared images.展开更多
Frequent extreme disasters have led to frequent large-scale power outages in recent years.To quickly restore power,it is necessary to understand the damage information of the distribution network accurately.However,th...Frequent extreme disasters have led to frequent large-scale power outages in recent years.To quickly restore power,it is necessary to understand the damage information of the distribution network accurately.However,the public network communication system is easily damaged after disasters,causing the operation center to lose control of the distribution network.In this paper,we considered using satellites to transmit the distribution network data and focus on the resource scheduling problem of the satellite emergency communication system for the distribution network.Specifically,this paper first formulates the satellite beam-pointing problem and the accesschannel joint resource allocation problem.Then,this paper proposes the Priority-based Beam-pointing and Access-Channel joint optimization algorithm(PBAC),which uses convex optimization theory to solve the satellite beam pointing problem,and adopts the block coordinate descent method,Lagrangian dual method,and a greedy algorithm to solve the access-channel joint resource allocation problem,thereby obtaining the optimal resource scheduling scheme for the satellite network.Finally,this paper conducts comparative experiments with existing methods to verify the effec-tiveness of the proposed methods.The results show that the total weighted transmitted data of the proposed algorithm is increased by about 19.29∼26.29%compared with other algorithms.展开更多
BACKGROUND Child vaccination plays a great role in preventing infectious diseases in children.While Ethiopia has emphasized child vaccination,its effectiveness largely depends on efficient communication between health...BACKGROUND Child vaccination plays a great role in preventing infectious diseases in children.While Ethiopia has emphasized child vaccination,its effectiveness largely depends on efficient communication between health practitioners and mothers/caregivers.Thus,sufficient communication contributes to promoting child immunization and in turn improving child health.AIM To examine child vaccine communication practices and strategies as well as their relationship with sociodemographic characteristics of respondents in the Amhara region of Ethiopia.METHODS A quantitative cross-sectional survey was conducted using a pretested Likert scale questionnaire and distributed to 123 health workers in primary healthcare centers between April 2024 and June 2024.The data were analyzed using both descriptive and inferential statistics.RESULTS The results indicated that the most common vaccine communication activities included education and communication(mean score=24.1),vaccine data registration(mean score=8.86),and information exchange(mean score=8.3).A significant correlation was found between the implementation of interpersonal health communication principles and immunization communication training(F=341.756,P=0.000,P<0.05).However,no significant correlations were observed between age,education,work experience,and vaccine communication practices.Additionally,the study found that the application of interpersonal communication principles was associated with the perceived relevance of immunization communication(F=27.790,P=0.000,P<0.05).CONCLUSION Based on the findings the study concluded that communication practice in promoting child immunization is insufficient.To enhance vaccine acceptance,continuous immunization communication training for health workers is recommended.展开更多
文摘As an important mechanism in multi-agent interaction,communication can make agents form complex team relationships rather than constitute a simple set of multiple independent agents.However,the existing communication schemes can bring much timing redundancy and irrelevant messages,which seriously affects their practical application.To solve this problem,this paper proposes a targeted multiagent communication algorithm based on state control(SCTC).The SCTC uses a gating mechanism based on state control to reduce the timing redundancy of communication between agents and determines the interaction relationship between agents and the importance weight of a communication message through a series connection of hard-and self-attention mechanisms,realizing targeted communication message processing.In addition,by minimizing the difference between the fusion message generated from a real communication message of each agent and a fusion message generated from the buffered message,the correctness of the final action choice of the agent is ensured.Our evaluation using a challenging set of Star Craft II benchmarks indicates that the SCTC can significantly improve the learning performance and reduce the communication overhead between agents,thus ensuring better cooperation between agents.
基金The work is a part of project named ＂＇Multi- Agent based Train Operation in Moving Block Setup＂ funded by Department of Information Technology （DIT）, Ministry of Commu- nications and Information Technology, Government of India, vide Grant Number 2（6）/2010-EC dated 21/03/2011.
文摘Multi-agent technology has been used in many complex distributed and concurrent systems. A railway system is such a safety critical system and careful inves- tigation of the functional components is very important. Study of the various functional components in communi- cation-based train control （CBTC） system necessitates a good structural design followed by its validation and ver- ification through a formal modelling technique. The work presented here is the follow up of our multi-agent-based CBTC system for Indian railway designed using the methodology for engineering system of software agents. Behavioural analysis of the designed system involves several operating scenarios that arise during train run, and helps in understanding the reaction of the system to such situations. This validation and verification are very important as it allows the system designer to critically evaluate the desired function of the system and to correct the design errors, if any, before its actual implementation. Modelling, validation and verification of the structural design through Coloured petri net （CPN） are central to this paper. Analysis of simulation results validates the efficacy of the design.
基金supported in part by the National Natural Science Foundation of China(No.61906156).
文摘This paper studies the problem of jamming decision-making for dynamic multiple communication links in wireless communication networks(WCNs).We propose a novel jamming channel allocation and power decision-making(JCAPD)approach based on multi-agent deep reinforcement learning(MADRL).In high-dynamic and multi-target aviation communication environments,the rapid changes in channels make it difficult for sensors to accurately capture instantaneous channel state information.This poses a challenge to make centralized jamming decisions with single-agent deep reinforcement learning(DRL)approaches.In response,we design a distributed multi-agent decision architecture(DMADA).We formulate multi-jammer resource allocation as a multiagent Markov decision process(MDP)and propose a fingerprint-based double deep Q-Network(FBDDQN)algorithm for solving it.Each jammer functions as an agent that interacts with the environment in this framework.Through the design of a reasonable reward and training mechanism,our approach enables jammers to achieve distributed cooperation,significantly improving the jamming success rate while considering jamming power cost,and reducing the transmission rate of links.Our experimental results show the FBDDQN algorithm is superior to the baseline methods.
基金supported in part by the National Natural Science Foundation of China(62273255,62350003,62088101)the Shanghai Science and Technology Cooperation Project(22510712000,21550760900)+1 种基金the Shanghai Municipal Science and Technology Major Project(2021SHZDZX0100)the Fundamental Research Funds for the Central Universities
文摘Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present some sufficient conditions for the exponential stability of a particular category of switched systems.
基金supported by the Key Laboratory of Near Ground Detection and Perception Technology(No.6142414220406 and 6142414210101)Shaanxi and Taicang Keypoint Research and Invention Program(No.2021GXLH-01-15 and TC2019SF03)。
文摘Intelligent Reflecting Surface(IRS),with the potential capability to reconstruct the electromagnetic propagation environment,evolves a new IRSassisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals leakage.However,when multiple IRSs are involved,accurate channel estimation is still a challenge due to the extra hardware complexity and communication overhead.Besides the crossinterference caused by massive reflecting paths,it is hard to obtain the close-formed solution for the optimization of covert communications.On this basis,the paper improves a heterogeneous multi-agent deep deterministic policy gradient(MADDPG)approach for the joint active and passive beamforming(Joint A&P BF)optimization without the channel estimation,where the base station(BS)and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency(CSE)cooperatively.Thanks to the‘centralized training and distributed execution’feature of MADDPG,each agent can execute the active or passive beamforming independently based on its partial observation without referring to others.Numeral results demonstrate that the proposed deep reinforcement learning(DRL)approach could not only obtain a preferable CSE of legitimate users and a low detection of probability(LPD)of warden,but also alleviate the communication overhead and simplify the IRSs deployment.
基金Supported by the National Natural Science Foundation of China(91016017)the National Aviation Found of China(20115868009)~~
文摘The cooperative control and stability analysis problems for the multi-agent system with sampled com- munication are investigated. Distributed state feedback controllers are adopted for the cooperation of networked agents. A theorem in the form of linear matrix inequalities（LMI） is derived to analyze the system stability. An- other theorem in the form of optimization problem subject to LMI constraints is proposed to design the controller, and then the algorithm is presented. The simulation results verify the validity and the effectiveness of the pro- posed approach.
基金The National Natural Science Foundation of China(62136008,62293541)The Beijing Natural Science Foundation(4232056)The Beijing Nova Program(20240484514).
文摘Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.
基金The National Natural Science Foundation of China(W2431048)The Science and Technology Research Program of Chongqing Municipal Education Commission,China(KJZDK202300807)The Chongqing Natural Science Foundation,China(CSTB2024NSCQQCXMX0052).
文摘This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.
基金supported by the National Natural Science Foundation of China(Nos.12272104,U22B2013).
文摘This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.
基金supported by the CAS Project for Young Scientists in Basic Research under Grant YSBR-035Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2.
文摘In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.
文摘With the boom in maritime activities,the need for highly reliable maritime communication is becoming urgent,which is an important component of 5G/6G communication networks.However,the bandwidth reuse characteristic of 5G/6G networks will inevitably lead to severe interference,resulting in degradation in the communication performance of maritime users.In this paper,we propose a safe deep reinforcement learning based interference coordination scheme to jointly optimize the power control and bandwidth allocation in maritime communication systems,and exploit the quality-of-service requirements of users as the risk value references to evaluate the communication policies.In particular,this scheme designs a deep neural network to select the communication policies through the evaluation network and update the parameters using the target network,which improves the communication performance and speeds up the convergence rate.Moreover,the Nash equilibrium of the interference coordination game and the computational complexity of the proposed scheme are analyzed.Simulation and experimental results verify the performance gain of the proposed scheme compared with benchmarks.
基金National Natural Science Foundation of China(U2468201,62122012,62221001).
文摘The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport industry must innovate in key technologies to ensure high-quality transmissions for passengers and railway operations.These systems must function effectively under high mobility conditions while prioritizing safety,ecofriendliness,comfort,transparency,predictability,and reliability.On the other hand,the proposal of 6 G wireless technology introduces new possibilities for innovation in communication technologies,which may truly realize the current vision of HSR.Therefore,this article gives a review of the current advanced 6 G wireless communication technologies for HSR,including random access and switching,channel estimation and beamforming,integrated sensing and communication,and edge computing.The main application scenarios of these technologies are reviewed,as well as their current research status and challenges,followed by an outlook on future development directions.
基金supported by the National Natural Science Foundation of China(No.62371080 and 62031006)the National Science Foundation of Chongqing,China(No.CSTB2022NSCQ-MSX0597)the Venture&Innovation Support Program for Chongqing Overseas Returnees,China(No.cx2022063)。
文摘This paper presents a design method to implement an antenna array characterized by ultra-wide beam coverage,low profile,and low Sidelobe Level(SLL)for the application of Unmanned Aerial Vehicle(UAV)air-to-ground communication.The array consists of ten broadside-radiating,ultrawide-beamwidth elements that are cascaded by a central-symmetry series-fed network with tapered currents following Dolph-Chebyshev distribution to provide low SLL.First,an innovative design of end-fire Huygens source antenna that is compatible with metal ground is presented.A low-profile,half-mode Microstrip Patch Antenna(MPA)is utilized to serve as the magnetic dipole and a monopole is utilized to serves as the electric dipole,constructing the compact,end-fire,grounded Huygens source antenna.Then,two opposite-oriented end-fire Huygens source antennas are seamlessly integrated into a single antenna element in the form of monopole-loaded MPA to accomplish the ultrawide,broadside-radiating beam.Particular consideration has been applied into the design of series-fed network as well as antenna element to compensate the adverse coupling effects between elements on the radiation performance.Experiment indicates an ultrawide Half-Power Beamwidth(HPBW)of 161°and a low SLL of-25 dB with a high gain of 12 d Bi under a single-layer configuration.The concurrent ultrawide beamwidth and low SLL make it particularly attractive for applications of UAV air-to-ground communication.
基金supported by the National Natural Science Foundation of China(52007009)Natural Science Foundation of Excellent Youth Project of Hunan Province of China(2023JJ20039)Science and Technology Projects of State Grid Hunan Provincial Electric Power Co.,Ltd.(5216A522001K,SGHNDK00PWJS2310173).
文摘In the islanded operation of distribution networks,due to the mismatch of line impedance at the inverter output,conventional droop control leads to inaccurate power sharing according to capacity,resulting in voltage and frequency fluctuations under minor external disturbances.To address this issue,this paper introduces an enhanced scheme for power sharing and voltage-frequency control.First,to solve the power distribution problem,we propose an adaptive virtual impedance control based on multi-agent consensus,which allows for precise active and reactive power allocation without requiring feeder impedance knowledge.Moreover,a novel consensus-based voltage and frequency control is proposed to correct the voltage deviation inherent in droop control and virtual impedance methods.This strategy maintains voltage and frequency stability even during communication disruptions and enhances system robustness.Additionally,a small-signal model is established for system stability analysis,and the control parameters are optimized.Simulation results validate the effectiveness of the proposed control scheme.
基金supported in part by the National Natural Science Foundation of China under Grants 62001225,62071236,62071234 and U22A2002in part by the Major Science and Technology plan of Hainan Province under Grant ZDKJ2021022+1 种基金in part by the Scientific Research Fund Project of Hainan University under Grant KYQD(ZR)-21008in part by the Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Industry)under Grants BE2023022 and BE2023022-2.
文摘The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defending against jamming and interference through spectrum allocation becomes challenging,especially when each UAV pair makes decisions independently.In this paper,we propose a cooperative multi-agent reinforcement learning(MARL)-based anti-jamming framework for I-UAVs,enabling UAV pairs to learn their own policies cooperatively.Specifically,we first model the problem as a modelfree multi-agent Markov decision process(MAMDP)to maximize the long-term expected system throughput.Then,for improving the exploration of the optimal policy,we resort to optimizing a MARL objective function with a mutual-information(MI)regularizer between states and actions,which can dynamically assign the probability for actions frequently used by the optimal policy.Next,through sharing their current channel selections and local learning experience(their soft Q-values),the UAV pairs can learn their own policies cooperatively relying on only preceding observed information and predicting others’actions.Our simulation results show that for both sweep jamming and Markov jamming patterns,the proposed scheme outperforms the benchmarkers in terms of throughput,convergence and stability for different numbers of jammers,channels and UAV pairs.
文摘Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investigate the integrated scheduling of communication,sensing,and control for UAV-aided FSO communication systems.Initially,a sensing-control model is established via the control theory.Moreover,an FSO communication channel model is established by considering the effects of atmospheric loss,atmospheric turbulence,geometrical loss,and angle-of-arrival fluctuation.Then,the relationship between the motion control of the UAV and radial displacement is obtained to link the control aspect and communication aspect.Assuming that the base station has instantaneous channel state information(CSI)or statistical CSI,the thresholds of the sensing-control pattern activation are designed,respectively.Finally,an integrated scheduling scheme for performing communication,sensing,and control is proposed.Numerical results indicate that,compared with conventional time-triggered scheme,the proposed integrated scheduling scheme obtains comparable communication and control performance,but reduces the sensing consumed power by 52.46%.
基金Supported by the Short-wave Infrared Camera Systems(B025F40622024)。
文摘The accuracy of spot centroid positioning has a significant impact on the tracking accuracy of the system and the stability of the laser link construction.In satellite laser communication systems,the use of short-wave infrared wavelengths as beacon light can reduce atmospheric absorption and signal attenuation.However,there are strong non-uniformity and blind pixels in the short-wave infrared image,which makes the image distorted and leads to the decrease of spot centroid positioning accuracy.Therefore,the high-precision localization of the spot centroid of the short-wave infrared images is of great research significance.A high-precision spot centroid positioning model for short-wave infrared is proposed to correct for non-uniformity and blind pixels in short-wave infrared images and quantify the localization errors caused by the two,further model-based localization error simulations are performed,and a novel spot centroid positioning payload for satellite laser communications has been designed using the latest 640×512 planar array InGaAs shortwave infrared detector.The experimental results show that the non-uniformity of the corrected image is reduced from 7%to 0.6%,the blind pixels rejection rate reaches 100%,the frame rate can be up to 2000 Hz,and the spot centroid localization accuracy is as high as 0.1 pixel point,which realizes high-precision spot centroid localization of high-frame-frequency short-wave infrared images.
基金supported by the Science and Technology Project of the State Grid Corporation of China(5400-202255158A-1-1-ZN).
文摘Frequent extreme disasters have led to frequent large-scale power outages in recent years.To quickly restore power,it is necessary to understand the damage information of the distribution network accurately.However,the public network communication system is easily damaged after disasters,causing the operation center to lose control of the distribution network.In this paper,we considered using satellites to transmit the distribution network data and focus on the resource scheduling problem of the satellite emergency communication system for the distribution network.Specifically,this paper first formulates the satellite beam-pointing problem and the accesschannel joint resource allocation problem.Then,this paper proposes the Priority-based Beam-pointing and Access-Channel joint optimization algorithm(PBAC),which uses convex optimization theory to solve the satellite beam pointing problem,and adopts the block coordinate descent method,Lagrangian dual method,and a greedy algorithm to solve the access-channel joint resource allocation problem,thereby obtaining the optimal resource scheduling scheme for the satellite network.Finally,this paper conducts comparative experiments with existing methods to verify the effec-tiveness of the proposed methods.The results show that the total weighted transmitted data of the proposed algorithm is increased by about 19.29∼26.29%compared with other algorithms.
文摘BACKGROUND Child vaccination plays a great role in preventing infectious diseases in children.While Ethiopia has emphasized child vaccination,its effectiveness largely depends on efficient communication between health practitioners and mothers/caregivers.Thus,sufficient communication contributes to promoting child immunization and in turn improving child health.AIM To examine child vaccine communication practices and strategies as well as their relationship with sociodemographic characteristics of respondents in the Amhara region of Ethiopia.METHODS A quantitative cross-sectional survey was conducted using a pretested Likert scale questionnaire and distributed to 123 health workers in primary healthcare centers between April 2024 and June 2024.The data were analyzed using both descriptive and inferential statistics.RESULTS The results indicated that the most common vaccine communication activities included education and communication(mean score=24.1),vaccine data registration(mean score=8.86),and information exchange(mean score=8.3).A significant correlation was found between the implementation of interpersonal health communication principles and immunization communication training(F=341.756,P=0.000,P<0.05).However,no significant correlations were observed between age,education,work experience,and vaccine communication practices.Additionally,the study found that the application of interpersonal communication principles was associated with the perceived relevance of immunization communication(F=27.790,P=0.000,P<0.05).CONCLUSION Based on the findings the study concluded that communication practice in promoting child immunization is insufficient.To enhance vaccine acceptance,continuous immunization communication training for health workers is recommended.
