The integrated communication and jamming(ICAJ)system recently has been proposed to enable communication and jamming(C&J)to reinforce each other in one system.By exploiting the diversity gain of multiple input mult...The integrated communication and jamming(ICAJ)system recently has been proposed to enable communication and jamming(C&J)to reinforce each other in one system.By exploiting the diversity gain of multiple input multiple output(MIMO)technology,a specific implementation form of ICAJ system,called communication-aided collaborative jamming system,is designed to transmit C&J signals at the same time and frequency.Different from previous studies which overlook the jamming prior information acquisition process and assume that the prior information is perfect or with bounded error,this paper takes the non-cooperative characteristics of jamming and the consequent difficulty in prior information acquisition into consideration.To analyze the tradeoff between C&J,the integration metric is proposed and then the corresponding system design problem is formulated.However,the non-convexity of problem and the lack of jamming prior information make the optimization tricky.In this case,blind channel estimation(BCE)is introduced to obtain an approximate channel state information(CSI)without interacting with jamming targets and then the neural network embedded with system performance calculation model is developed to establish the correspondence between the estimated CSI and optimal beamforming design.Furthermore,a hybrid data-driven and model-based approach,blind channel estimation-deep learning(BCEDL),is proposed to accomplish the beamforming design based on unsupervised learning for ICAJ system in non-cooperative scenarios.The simulation results show that the BCE-DL algorithm outperforms the conventional algorithms in the presence of CSI estimation errors and is a flexible approach which takes the best of both data-driven and model-based methods to design the ICAJ system.展开更多
The coordinated optimization problem of the electricity-gas-heat integrated energy system(IES)has the characteristics of strong coupling,non-convexity,and nonlinearity.The centralized optimization method has a high co...The coordinated optimization problem of the electricity-gas-heat integrated energy system(IES)has the characteristics of strong coupling,non-convexity,and nonlinearity.The centralized optimization method has a high cost of communication and complex modeling.Meanwhile,the traditional numerical iterative solution cannot deal with uncertainty and solution efficiency,which is difficult to apply online.For the coordinated optimization problem of the electricity-gas-heat IES in this study,we constructed a model for the distributed IES with a dynamic distribution factor and transformed the centralized optimization problem into a distributed optimization problem in the multi-agent reinforcement learning environment using multi-agent deep deterministic policy gradient.Introducing the dynamic distribution factor allows the system to consider the impact of changes in real-time supply and demand on system optimization,dynamically coordinating different energy sources for complementary utilization and effectively improving the system economy.Compared with centralized optimization,the distributed model with multiple decision centers can achieve similar results while easing the pressure on system communication.The proposed method considers the dual uncertainty of renewable energy and load in the training.Compared with the traditional iterative solution method,it can better cope with uncertainty and realize real-time decision making of the system,which is conducive to the online application.Finally,we verify the effectiveness of the proposed method using an example of an IES coupled with three energy hub agents.展开更多
With the development of artificial intelligence(AI)and 5G technology,the integration of sensing,communication and computing in the Internet of Vehicles(Io V)is becoming a trend.However,the large amount of data transmi...With the development of artificial intelligence(AI)and 5G technology,the integration of sensing,communication and computing in the Internet of Vehicles(Io V)is becoming a trend.However,the large amount of data transmission and the computing requirements of intelligent tasks lead to the complex resource management problems.In view of the above challenges,this paper proposes a tasks-oriented joint resource allocation scheme(TOJRAS)in the scenario of Io V.First,this paper proposes a system model with sensing,communication,and computing integration for multiple intelligent tasks with different requirements in the Io V.Secondly,joint resource allocation problems for real-time tasks and delay-tolerant tasks in the Io V are constructed respectively,including communication,computing and caching resources.Thirdly,a distributed deep Q-network(DDQN)based algorithm is proposed to solve the optimization problems,and the convergence and complexity of the algorithm are discussed.Finally,the experimental results based on real data sets verify the performance advantages of the proposed resource allocation scheme,compared to the existing ones.The exploration efficiency of our proposed DDQN-based algorithm is improved by at least about 5%,and our proposed resource allocation scheme improves the m AP performance by about 0.15 under resource constraints.展开更多
Purpose:In this paper,we develop a heterogeneous graph network using citation relations between papers and their basic information centered around the“Paper mills”papers under withdrawal observation,and we train gra...Purpose:In this paper,we develop a heterogeneous graph network using citation relations between papers and their basic information centered around the“Paper mills”papers under withdrawal observation,and we train graph neural network models and classifiers on these heterogeneous graphs to classify paper nodes.Design/methodology/approach:Our proposed citation network-based“Paper mills”detection model(PDCN model for short)integrates textual features extracted from the paper titles using the BERT model with structural features obtained from analyzing the heterogeneous graph through the heterogeneous graph attention network model.Subsequently,these features are classified using LGBM classifiers to identify“Paper mills”papers.Findings:On our custom dataset,the PDCN model achieves an accuracy of 81.85%and an F1-score of 80.49%in the“Paper mills”detection task,representing a significant improvement in performance compared to several baseline models.Research limitations:We considered only the title of the article as a text feature and did not obtain features for the entire article.Practical implications:The PDCN model we developed can effectively identify“Paper mills”papers and is suitable for the automated detection of“Paper mills”during the review process.Originality/value:We incorporated both text and citation detection into the“Paper mills”identification process.Additionally,the PDCN model offers a basis for judgment and scientific guidance in recognizing“Paper mills”papers.展开更多
Due to the broadcast nature of wireless channels and the development of quantum computers,the confidentiality of wireless communication is seriously threatened.In this paper,we propose an integrated communications and...Due to the broadcast nature of wireless channels and the development of quantum computers,the confidentiality of wireless communication is seriously threatened.In this paper,we propose an integrated communications and security(ICAS)design to enhance communication security using reconfigurable intelligent surfaces(RIS),in which the physical layer key generation(PLKG)rate and the data transmission rate are jointly considered.Specifically,to deal with the threat of eavesdropping attackers,we focus on studying the simultaneous transmission and key generation(STAG)by configuring the RIS phase shift.Firstly,we derive the key generation rate of the RIS assisted PLKG and formulate the optimization problem.Then,in light of the dynamic wireless environments,the optimization problem is modeled as a finite Markov decision process.We put forward a policy gradient-based proximal policy optimization(PPO)algorithm to optimize the continuous phase shift of the RIS,which improves the convergence stability and explores the security boundary of the RIS phase shift for STAG.The simulation results demonstrate that the proposed algorithm outperforms the benchmark method in convergence stability and system performance.By reasonably allocating the weight factors for the data transmission rate and the key generation rate,“one-time pad”communication can be achieved.The proposed method has about 90%performance improvement for“one-time pad”communication compared with the benchmark methods.展开更多
基金supported by the National Natural Science Foundation of China(No.62171462,No.62401626,No.62271501)the Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Industry)under Grants BE2023022 and BE2023022-4the Natural Science Foundation of Jiangsu Province(No.BK20240200)。
文摘The integrated communication and jamming(ICAJ)system recently has been proposed to enable communication and jamming(C&J)to reinforce each other in one system.By exploiting the diversity gain of multiple input multiple output(MIMO)technology,a specific implementation form of ICAJ system,called communication-aided collaborative jamming system,is designed to transmit C&J signals at the same time and frequency.Different from previous studies which overlook the jamming prior information acquisition process and assume that the prior information is perfect or with bounded error,this paper takes the non-cooperative characteristics of jamming and the consequent difficulty in prior information acquisition into consideration.To analyze the tradeoff between C&J,the integration metric is proposed and then the corresponding system design problem is formulated.However,the non-convexity of problem and the lack of jamming prior information make the optimization tricky.In this case,blind channel estimation(BCE)is introduced to obtain an approximate channel state information(CSI)without interacting with jamming targets and then the neural network embedded with system performance calculation model is developed to establish the correspondence between the estimated CSI and optimal beamforming design.Furthermore,a hybrid data-driven and model-based approach,blind channel estimation-deep learning(BCEDL),is proposed to accomplish the beamforming design based on unsupervised learning for ICAJ system in non-cooperative scenarios.The simulation results show that the BCE-DL algorithm outperforms the conventional algorithms in the presence of CSI estimation errors and is a flexible approach which takes the best of both data-driven and model-based methods to design the ICAJ system.
基金supported by The National Key R&D Program of China(2020YFB0905900):Research on artificial intelligence application of power internet of things.
文摘The coordinated optimization problem of the electricity-gas-heat integrated energy system(IES)has the characteristics of strong coupling,non-convexity,and nonlinearity.The centralized optimization method has a high cost of communication and complex modeling.Meanwhile,the traditional numerical iterative solution cannot deal with uncertainty and solution efficiency,which is difficult to apply online.For the coordinated optimization problem of the electricity-gas-heat IES in this study,we constructed a model for the distributed IES with a dynamic distribution factor and transformed the centralized optimization problem into a distributed optimization problem in the multi-agent reinforcement learning environment using multi-agent deep deterministic policy gradient.Introducing the dynamic distribution factor allows the system to consider the impact of changes in real-time supply and demand on system optimization,dynamically coordinating different energy sources for complementary utilization and effectively improving the system economy.Compared with centralized optimization,the distributed model with multiple decision centers can achieve similar results while easing the pressure on system communication.The proposed method considers the dual uncertainty of renewable energy and load in the training.Compared with the traditional iterative solution method,it can better cope with uncertainty and realize real-time decision making of the system,which is conducive to the online application.Finally,we verify the effectiveness of the proposed method using an example of an IES coupled with three energy hub agents.
基金supported by The Fundamental Research Funds for the Central Universities(No.2021XD-A01-1)The National Natural Science Foundation of China(No.92067202)。
文摘With the development of artificial intelligence(AI)and 5G technology,the integration of sensing,communication and computing in the Internet of Vehicles(Io V)is becoming a trend.However,the large amount of data transmission and the computing requirements of intelligent tasks lead to the complex resource management problems.In view of the above challenges,this paper proposes a tasks-oriented joint resource allocation scheme(TOJRAS)in the scenario of Io V.First,this paper proposes a system model with sensing,communication,and computing integration for multiple intelligent tasks with different requirements in the Io V.Secondly,joint resource allocation problems for real-time tasks and delay-tolerant tasks in the Io V are constructed respectively,including communication,computing and caching resources.Thirdly,a distributed deep Q-network(DDQN)based algorithm is proposed to solve the optimization problems,and the convergence and complexity of the algorithm are discussed.Finally,the experimental results based on real data sets verify the performance advantages of the proposed resource allocation scheme,compared to the existing ones.The exploration efficiency of our proposed DDQN-based algorithm is improved by at least about 5%,and our proposed resource allocation scheme improves the m AP performance by about 0.15 under resource constraints.
基金supported by the National Science Foundation of China(Grant No.62176026)Project of“Image Inspection Basic Data and Platform Construction”,Department of Science and Technology Supervision and Integrity Building,Ministry of Science and Technology(Grant No.GXCZ-D-21070106)ISTIC-Taylor&Francis Group Academic Frontier Watch Joint Laboratory Open Grant.
文摘Purpose:In this paper,we develop a heterogeneous graph network using citation relations between papers and their basic information centered around the“Paper mills”papers under withdrawal observation,and we train graph neural network models and classifiers on these heterogeneous graphs to classify paper nodes.Design/methodology/approach:Our proposed citation network-based“Paper mills”detection model(PDCN model for short)integrates textual features extracted from the paper titles using the BERT model with structural features obtained from analyzing the heterogeneous graph through the heterogeneous graph attention network model.Subsequently,these features are classified using LGBM classifiers to identify“Paper mills”papers.Findings:On our custom dataset,the PDCN model achieves an accuracy of 81.85%and an F1-score of 80.49%in the“Paper mills”detection task,representing a significant improvement in performance compared to several baseline models.Research limitations:We considered only the title of the article as a text feature and did not obtain features for the entire article.Practical implications:The PDCN model we developed can effectively identify“Paper mills”papers and is suitable for the automated detection of“Paper mills”during the review process.Originality/value:We incorporated both text and citation detection into the“Paper mills”identification process.Additionally,the PDCN model offers a basis for judgment and scientific guidance in recognizing“Paper mills”papers.
基金supported in part by the National Science Foundation of China(NSFC)under Grant No.62371131in part by the National Key R&D Program of China under Grant No.2024YFE0200700in part by the program of Zhishan Young Scholar of Southeast University under Grant No.2242024RCB0030。
文摘Due to the broadcast nature of wireless channels and the development of quantum computers,the confidentiality of wireless communication is seriously threatened.In this paper,we propose an integrated communications and security(ICAS)design to enhance communication security using reconfigurable intelligent surfaces(RIS),in which the physical layer key generation(PLKG)rate and the data transmission rate are jointly considered.Specifically,to deal with the threat of eavesdropping attackers,we focus on studying the simultaneous transmission and key generation(STAG)by configuring the RIS phase shift.Firstly,we derive the key generation rate of the RIS assisted PLKG and formulate the optimization problem.Then,in light of the dynamic wireless environments,the optimization problem is modeled as a finite Markov decision process.We put forward a policy gradient-based proximal policy optimization(PPO)algorithm to optimize the continuous phase shift of the RIS,which improves the convergence stability and explores the security boundary of the RIS phase shift for STAG.The simulation results demonstrate that the proposed algorithm outperforms the benchmark method in convergence stability and system performance.By reasonably allocating the weight factors for the data transmission rate and the key generation rate,“one-time pad”communication can be achieved.The proposed method has about 90%performance improvement for“one-time pad”communication compared with the benchmark methods.
