This paper investigates the channel diversity problem in high frequency(HF) communication systems. Due to the limited HF spectrum resources, a HF communication system with shared channels is considered, where each use...This paper investigates the channel diversity problem in high frequency(HF) communication systems. Due to the limited HF spectrum resources, a HF communication system with shared channels is considered, where each user equipment(UE) has individual communication demand. In order to maximize the communication probability of the whole system, a matching-potential game framework is designed. In detail, the channel diversity problem is decomposed into two sub-problems. One is channel-transmitter matching problem, which can be formulated as a many-to-one matching game. The other is the transmitter allocation problem which decides the transmission object that each transmitter communicates with under channel-transmitter matching result, and this sub-problem can be modeled as a potential game. A multiple round stable matching algorithm(MRSMA) is proposed, which obtains a stable matching result for the first sub-problem, and a distributed BR-based transmitter allocation algorithm(DBRTAA) is designed to reach Nash Equilibrium(NE) of the second sub-problem. Simulation results verify the effectiveness and superiority of the proposed method.展开更多
In this paper,we consider distributed Nash equilibrium(NE)seeking in potential games over a multi-agent network,where each agent can not observe the actions of all its rivals.Based on the best response dynamics,we des...In this paper,we consider distributed Nash equilibrium(NE)seeking in potential games over a multi-agent network,where each agent can not observe the actions of all its rivals.Based on the best response dynamics,we design a distributed NE seeking algorithm by incorporating the non-smooth finite-time average tracking dynamics,where each agent only needs to know its own action and exchange information with its neighbours through a communication graph.We give a sufficient condition for the Lipschitz continuity of the best response mapping for potential games,and then prove the convergence of the proposed algorithm based on the Lyapunov theory.Numerical simulations are given to verify the resultandillustrate the effectiveness of the algorithm.展开更多
A cross-layer resource allocation scheme based on potential game(CLRA_ PG) is proposed for the downlink multi-cell orthogonal frequency-division multiple-access(OFDMA) system with universal frequency reuse.As a method...A cross-layer resource allocation scheme based on potential game(CLRA_ PG) is proposed for the downlink multi-cell orthogonal frequency-division multiple-access(OFDMA) system with universal frequency reuse.As a method to mitigate inter-cell interference(ICI),base station coordination has been considered.In the process of the objective function modeling,this paper adopts a pricing mechanism which not only maximizes the individual utility but also considers the interference to other users.Based on the potential game theory,the objective problem is converted to a potential function which can be easily solved.The Karush-Kuhn-Tucker(KKT) conditions and the iterative water-filling algorithm are employed to solve the constraint objective optimization problem.Moreover,extensive simulations are conducted to evaluate how the pricing factors affect the algorithm.At the same time,comparing with the traditional policy,our simulation results show that the proposed scheme can significantly improve the performance of the system.展开更多
The motion planning problem for multi-agent systems becomes particularly challenging when humans or human-controlled robots are present in a mixed environment.To address this challenge,this paper presents an interacti...The motion planning problem for multi-agent systems becomes particularly challenging when humans or human-controlled robots are present in a mixed environment.To address this challenge,this paper presents an interaction-aware motion planning approach based on game theory in a receding-horizon manner Leveraging the framework provided by dynamic potential games for handling the interactions among agents,this approach formulates the multi-agent motion planning problem as a differential potential game,highlighting the effectiveness of constrained potential games in facilitating interactive motion planning among agents.Furthermore,online learning techniques are incorporated to dynamically learn the unknown preferences and models of humans or human-controlled robots through the analysis of observed data.To evaluate the effectiveness of the proposed approach,numerical simulations are conducted,demonstrating its capability to generate interactive trajectories for all agents,including humans and human-controlled agents,operating within the mixed environment.The simulation results illustrate the effectiveness of the proposed approach in handling the complexities of multi-agent motion planning in real-world scenarios.展开更多
Potential games are noncooperative games for which there exist auxiliary functions, called potentials,such that the maximizers of the potential are also Nash equilibria of the corresponding game. Some properties of Na...Potential games are noncooperative games for which there exist auxiliary functions, called potentials,such that the maximizers of the potential are also Nash equilibria of the corresponding game. Some properties of Nash equilibria, such as existence or stability, can be derived from the potential, whenever it exists. We survey different classes of potential games in the static and dynamic cases, with a finite number of players, as well as in population games where a continuum of players is allowed. Likewise, theoretical concepts and applications are discussed by means of illustrative examples.展开更多
The state-based potential game is discussed and a game-based approach is proposed for distributed optimization problem in this paper.A continuous-time model is employed to design the state dynamics and learning algori...The state-based potential game is discussed and a game-based approach is proposed for distributed optimization problem in this paper.A continuous-time model is employed to design the state dynamics and learning algorithms of the state-based potential game with Lagrangian multipliers as the states.It is shown that the stationary state Nash equilibrium of the designed game contains the optimal solution of the optimization problem.Moreover,the convergence and stability of the learning algorithms are obtained for both undirected and directed communication graph.Additionally,the application to plug-in electric vehicle management is also discussed.展开更多
This paper studies a class of strategic games,where players often collaborate with other players to form a group when making decisions,and the payoff functions of players in such games are presented as vector function...This paper studies a class of strategic games,where players often collaborate with other players to form a group when making decisions,and the payoff functions of players in such games are presented as vector functions.First,using the semi-tensor product(STP)method,it is proved that a finite game with vector payoffs is potential if and only if its potential equation has solution.By adding a suitable weight vector to the vector payoffs of each player,a finite game with vector payoffs that is not potential can be converted into a potential game.Second,as a natural generalization,the authors consider the verification problem of the group-based potential games with vector payoffs.By solving a linear potential equation,a simple formula is obtained to calculate the corresponding potential function.Finally,some examples are presented and discussed in detail to illustrate the theoretical results.展开更多
With the rapid development of Intelligent Transportation Systems(ITS),many new applications for Intelligent Connected Vehicles(ICVs)have sprung up.In order to tackle the conflict between delay-sensitive applications a...With the rapid development of Intelligent Transportation Systems(ITS),many new applications for Intelligent Connected Vehicles(ICVs)have sprung up.In order to tackle the conflict between delay-sensitive applications and resource-constrained vehicles,computation offloading paradigm that transfers computation tasks from ICVs to edge computing nodes has received extensive attention.However,the dynamic network conditions caused by the mobility of vehicles and the unbalanced computing load of edge nodes make ITS face challenges.In this paper,we propose a heterogeneous Vehicular Edge Computing(VEC)architecture with Task Vehicles(TaVs),Service Vehicles(SeVs)and Roadside Units(RSUs),and propose a distributed algorithm,namely PG-MRL,which jointly optimizes offloading decision and resource allocation.In the first stage,the offloading decisions of TaVs are obtained through a potential game.In the second stage,a multi-agent Deep Deterministic Policy Gradient(DDPG),one of deep reinforcement learning algorithms,with centralized training and distributed execution is proposed to optimize the real-time transmission power and subchannel selection.The simulation results show that the proposed PG-MRL algorithm has significant improvements over baseline algorithms in terms of system delay.展开更多
UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we inve...UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we investigate current works about UAV coverage problem and propose a multi-UAV coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games(EPG) and have Nash equilibrium(NE) points. Then the multi-UAV energy-efficient coverage deployment algorithm based on spatial adaptive play(MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.展开更多
This paper investigates the Quality of Experience(QoE)oriented channel access anti-jamming problem in 5th Generation Mobile Communication(5G)ultra-dense networks.Firstly,considering that the 5G base station adopts bea...This paper investigates the Quality of Experience(QoE)oriented channel access anti-jamming problem in 5th Generation Mobile Communication(5G)ultra-dense networks.Firstly,considering that the 5G base station adopts beamforming technology,an anti-jamming model under Space Division Multiple Access(SDMA)conditions is proposed.Secondly,the confrontational relationship between users and the jammer is formulated as a Stackelberg game.Besides,to achieve global optimization,we design a local cooperation mechanism for users and formulate the cooperation and competition among users as a local altruistic game.By proving that the local altruistic game is an Exact Potential Game(EPG),we further prove the existence of pure strategy Nash Equilibrium(NE)among users and Stackelberg Equilibrium(SE)between users and jammer.Thirdly,to obtain the equilibrium solutions of the proposed games,we propose an anti-jamming channel selection algorithm and improve its convergence speed through heterogeneous learning parameters.The simulation results validate the convergence and effectiveness of the proposed algorithm.Compared with the throughput optimization scheme,our proposed scheme obtain a greater network satisfaction rate.Finally,we also analyze user fairness changes during the algorithm convergence process and get some interesting conclusions.展开更多
Low earth orbit(LEO) satellite communications can provide ubiquitous and reliable services,making it an essential part of the Internet of Everything network. Beam hopping(BH) is an emerging technology for effectively ...Low earth orbit(LEO) satellite communications can provide ubiquitous and reliable services,making it an essential part of the Internet of Everything network. Beam hopping(BH) is an emerging technology for effectively addressing the issue of low resource utilization caused by the non-uniform spatio-temporal distribution of traffic demands. However, how to allocate multi-dimensional resources in a timely and efficient way for the highly dynamic LEO satellite systems remains a challenge. This paper proposes a joint beam scheduling and power optimization beam hopping(JBSPO-BH) algorithm considering the differences in the geographic distribution of sink nodes. The JBSPO-BH algorithm decouples the original problem into two sub-problems. The beam scheduling problem is modelled as a potential game,and the Nash equilibrium(NE) point is obtained as the beam scheduling strategy. Moreover, the penalty function interior point method is applied to optimize the power allocation. Simulation results show that the JBSPO-BH algorithm has low time complexity and fast convergence and achieves better performance both in throughput and fairness. Compared with greedybased BH, greedy-based BH with the power optimization, round-robin BH, Max-SINR BH and satellite resource allocation algorithm, the throughput of the proposed algorithm is improved by 44.99%, 20.79%,156.06%, 15.39% and 8.17%, respectively.展开更多
Haptic communications is recognized as a promising enabler of extensive services by enabling real-time haptic control and feedback in remote environments,e.g.,teleoperation and autonomous driving.Considering the stric...Haptic communications is recognized as a promising enabler of extensive services by enabling real-time haptic control and feedback in remote environments,e.g.,teleoperation and autonomous driving.Considering the strict transmission requirements on reliability and latency,Device-to-Device(D2D)communications is introduced to assist haptic communications.In particular,the teleoperators with poor channel quality are assisted by auxiliaries,and each auxiliary and its corresponding teleoperator constitute a D2D pair.However,the haptic interaction and the scarcity of radio resources pose severe challenges to the resource allocation,especially facing the sporadic packet arrivals.First,the contentionbased access scheme is applied to achieve low-latency transmission,where the resource scheduling latency is omitted and users can directly access available resources.In this context,we derive the reliability index of D2D pairs under the contention-based access scheme,i.e.,closed-loop packet error probability.Then,the reliability performance is guaranteed by bidirectional power control,which aims to minimize the sum packet error probability of all D2D pairs.Potential game theory is introduced to solve the problem with low complexity.Accordingly,a distributed power control algorithm based on synchronous log-linear learning is proposed to converge to the optimal Nash Equilibrium.Experimental results demonstrate the superiority of the proposed learning algorithm.展开更多
Mobile Edge Computing(MEC)is a pivotal technology that provides agile-response services by deploying computation and storage resources in proximity to end-users.However,resource-constrained edge servers fall victim to...Mobile Edge Computing(MEC)is a pivotal technology that provides agile-response services by deploying computation and storage resources in proximity to end-users.However,resource-constrained edge servers fall victim to Denial-of-Service(DoS)attacks easily.Failures to mitigate DoS attacks effectively hinder the delivery of reliable and sustainable edge services.Conventional DoS mitigation solutions in cloud computing environments are not directly applicable in MEC environments because their design did not factor in the unique characteristics of MEC environments,e.g.,constrained resources on edge servers and requirements for low service latency.Existing solutions mitigate edge DoS attacks by transferring user requests from edge servers under attacks to others for processing.Furthermore,the heterogeneity in end-users’resource demands can cause resource fragmentation on edge servers and undermine the ability of these solutions to mitigate DoS attacks effectively.User requests often have to be transferred far away for processing,which increases the service latency.To tackle this challenge,this paper presents a fragmentationaware gaming approach called HEDMGame that attempts to minimize service latency by matching user requests to edge servers’remaining resources while making request-transferring decisions.Through theoretical analysis and experimental evaluation,we validate the effectiveness and efficiency of HEDMGame,and demonstrate its superiority over the state-of-the-art solution.展开更多
The satellite-terrestrial cooperative network is considered an emerging network architecture,which can adapt to various services and applications in the future communication network.In recent years,the combination of ...The satellite-terrestrial cooperative network is considered an emerging network architecture,which can adapt to various services and applications in the future communication network.In recent years,the combination of satellite communication and Mobile Edge Computing(MEC)has become an emerging research hotspot.Satellite edge computing can provide users with full coverage on-orbit computing services by deploying MEC servers on satellites.This paper studies the task offloading of multi-user and multi-edge computing satellites and proposes a novel algorithm that joint task offloading and communication computing resource optimization(JTO-CCRO).The JTO-CCRO is decoupled into task offloading and resource allocation sub-problems.After the mutual iteration of the two sub-problems,the system utility function can be further reduced.For the task offloading sub-problem,it is first confirmed that the offloading problem is a game problem.The offloading strategy can be obtained from the Nash equilibrium solution.We confirm resource optimization sub-problem is a convex optimization problem that can be solved by the Lagrange multiplier method.Simulation shows that the JTO-CCRO algorithm can converge quickly and effectively reduce the system utility function.展开更多
With the fast development of Mobile Internet,data traffic generated by end devices is anticipated to witness substantial growth in the future years.However,processing tasks locally will cause latency due to the limite...With the fast development of Mobile Internet,data traffic generated by end devices is anticipated to witness substantial growth in the future years.However,processing tasks locally will cause latency due to the limited resources of the end devices.Edge-cloud collaboration,an effective solution for latency-sensitive applications,is attracting greater attention from both industry and academia.It combines the advantages of the cloud center with abundant computing resources and edge nodes with low-latency capabilities.In this paper,we propose a two-stage task offloading framework with edge-cloud collaboration to assist end devices processing latency-sensitive tasks either on the edge servers or in the cloud center.As for homogeneous task offloading,in the first stage,the competitive end devices offload tasks to the edge gateways.We formulate the selfish task offloading problem among end devices as a potential game.In the second stage,the edge nodes request resources from the cloud center to process end devices tasks due to their limited resources.Then,we consider the heterogeneous task offloading problem and use intelligent optimization algorithm to obtain the optimal offloading strategy.Simulation results show that the service prices of edge nodes influence the decisions and task offloading costs of end devices.We also verify the intelligent optimization algorithm can achieve optimal performance with low complexity and fast convergence.展开更多
This paper proposes a distributed task allocation algorithm based on game theory to solve the complex task allocation optimization problem when UAV clusters carry heterogeneous resources and tasks have heterogeneous d...This paper proposes a distributed task allocation algorithm based on game theory to solve the complex task allocation optimization problem when UAV clusters carry heterogeneous resources and tasks have heterogeneous demands. Considering the heterogeneity of resources,two pre-processing methods are proposed: one is the grouping algorithm that combines greedy algorithm with simulated annealing algorithm, and the other is the improved K-medoids clustering algorithm based on heterogeneous resources. These pre-process methods, through grouping and clustering, can reduce the complexity of task allocation. The entropy weight method is utilized to prioritize tasks based on multiple metrics. Considering task demands,airborne resources and path cost, a coalition formation game model is established, which is proved to be a potential game. Then a distributed task allocation algorithm based on coalition formation game is designed to address the task allocation problem. Finally, the simulation involving 30 tasks with heterogeneous requirements assigned to 100 UAVs validates the effectiveness of the proposed algorithm, showing that it can achieve good task allocation results with great real-time performance.展开更多
Nowadays the semi-tensor product(STP)approach to finite games has become a promising new direction.This paper provides a comprehensive survey on this prosperous field.After a brief introduction for STP and finite(netw...Nowadays the semi-tensor product(STP)approach to finite games has become a promising new direction.This paper provides a comprehensive survey on this prosperous field.After a brief introduction for STP and finite(networked)games,a description for the principle and fundamental technique of STP approach to finite games is presented.Then several problems and recent results about theory and applications of finite games via STP are presented.A brief comment about the potential use of STP to artificial intelligence is also proposed.展开更多
Mobile Edge Computing(MEC)has been envisioned as a promising distributed computing paradigm where mobile users offload their tasks to edge nodes to decrease the cost of energy and computation.However,most of the exist...Mobile Edge Computing(MEC)has been envisioned as a promising distributed computing paradigm where mobile users offload their tasks to edge nodes to decrease the cost of energy and computation.However,most of the existing studies only consider the congestion of wireless channels as a crucial factor affecting the strategy-making process,while ignoring the impact of offloading among edge nodes.In addition,centralized task offloading strategies result in enormous computation complexity in center nodes.Along this line,we take both the congestion of wireless channels and the offloading among multiple edge nodes into consideration to enrich users'offloading strategies and propose the Parallel User Selection Algorithm(PUS)and Single User Selection Algorithm(SUS)to substantially accelerate the convergence.More practically,we extend the users'offloading strategies to take into account idle devices and cloud services,which considers the potential computing resources at the edge.Furthermore,we construct a potential game in which each user selfishly seeks an optimal strategy to minimize its cost of latency and energy based on acceptable latency,and find the potential function to prove the existence of Nash equilibrium(NE).Additionally,we update PUS to accelerate its convergence and illustrate its performance through the experimental results of three real datasets,and the updated PUS effectively decreases the total cost and reaches Nash equilibrium.展开更多
基金supported by the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province under Grant No. BK20160034in part by the National Natural Science Foundation of China under Grant No. 61671473 and No. 61631020in part by the Open Research Foundation of Science and Technology on Communication Networks Laboratory
文摘This paper investigates the channel diversity problem in high frequency(HF) communication systems. Due to the limited HF spectrum resources, a HF communication system with shared channels is considered, where each user equipment(UE) has individual communication demand. In order to maximize the communication probability of the whole system, a matching-potential game framework is designed. In detail, the channel diversity problem is decomposed into two sub-problems. One is channel-transmitter matching problem, which can be formulated as a many-to-one matching game. The other is the transmitter allocation problem which decides the transmission object that each transmitter communicates with under channel-transmitter matching result, and this sub-problem can be modeled as a potential game. A multiple round stable matching algorithm(MRSMA) is proposed, which obtains a stable matching result for the first sub-problem, and a distributed BR-based transmitter allocation algorithm(DBRTAA) is designed to reach Nash Equilibrium(NE) of the second sub-problem. Simulation results verify the effectiveness and superiority of the proposed method.
基金This work was supported by the Shanghai Sailing Program(No.20YF1453000)the Fundamental Research Funds for the Central Universities(No.22120200048).
文摘In this paper,we consider distributed Nash equilibrium(NE)seeking in potential games over a multi-agent network,where each agent can not observe the actions of all its rivals.Based on the best response dynamics,we design a distributed NE seeking algorithm by incorporating the non-smooth finite-time average tracking dynamics,where each agent only needs to know its own action and exchange information with its neighbours through a communication graph.We give a sufficient condition for the Lipschitz continuity of the best response mapping for potential games,and then prove the convergence of the proposed algorithm based on the Lyapunov theory.Numerical simulations are given to verify the resultandillustrate the effectiveness of the algorithm.
基金Supported by the National Key Technology R&D Program of China(No.2010ZX03003-001-01,2011 ZX03003-002-01)National Natural Science Foundation of China(No.61101109)the Co-building Project of Beijing Municipal Education Commission"G-RAN based Experimental Platform for Future Mobile Communications"
文摘A cross-layer resource allocation scheme based on potential game(CLRA_ PG) is proposed for the downlink multi-cell orthogonal frequency-division multiple-access(OFDMA) system with universal frequency reuse.As a method to mitigate inter-cell interference(ICI),base station coordination has been considered.In the process of the objective function modeling,this paper adopts a pricing mechanism which not only maximizes the individual utility but also considers the interference to other users.Based on the potential game theory,the objective problem is converted to a potential function which can be easily solved.The Karush-Kuhn-Tucker(KKT) conditions and the iterative water-filling algorithm are employed to solve the constraint objective optimization problem.Moreover,extensive simulations are conducted to evaluate how the pricing factors affect the algorithm.At the same time,comparing with the traditional policy,our simulation results show that the proposed scheme can significantly improve the performance of the system.
基金supported by the A*STAR under its"RIE2025 IAF-PP Advanced ROS2-native Platform Technologies for Cross sectorial Robotics Adoption(M21K1a0104)"programme.
文摘The motion planning problem for multi-agent systems becomes particularly challenging when humans or human-controlled robots are present in a mixed environment.To address this challenge,this paper presents an interaction-aware motion planning approach based on game theory in a receding-horizon manner Leveraging the framework provided by dynamic potential games for handling the interactions among agents,this approach formulates the multi-agent motion planning problem as a differential potential game,highlighting the effectiveness of constrained potential games in facilitating interactive motion planning among agents.Furthermore,online learning techniques are incorporated to dynamically learn the unknown preferences and models of humans or human-controlled robots through the analysis of observed data.To evaluate the effectiveness of the proposed approach,numerical simulations are conducted,demonstrating its capability to generate interactive trajectories for all agents,including humans and human-controlled agents,operating within the mixed environment.The simulation results illustrate the effectiveness of the proposed approach in handling the complexities of multi-agent motion planning in real-world scenarios.
基金supported by Consejo Nacional de Ciencia y Tecnología of Mexico (Grant No. 221291)
文摘Potential games are noncooperative games for which there exist auxiliary functions, called potentials,such that the maximizers of the potential are also Nash equilibria of the corresponding game. Some properties of Nash equilibria, such as existence or stability, can be derived from the potential, whenever it exists. We survey different classes of potential games in the static and dynamic cases, with a finite number of players, as well as in population games where a continuum of players is allowed. Likewise, theoretical concepts and applications are discussed by means of illustrative examples.
基金This work was supported by the NNSF of China[grant number 61174071]by 973 Program[grant number 2014CB845301/2/3].
文摘The state-based potential game is discussed and a game-based approach is proposed for distributed optimization problem in this paper.A continuous-time model is employed to design the state dynamics and learning algorithms of the state-based potential game with Lagrangian multipliers as the states.It is shown that the stationary state Nash equilibrium of the designed game contains the optimal solution of the optimization problem.Moreover,the convergence and stability of the learning algorithms are obtained for both undirected and directed communication graph.Additionally,the application to plug-in electric vehicle management is also discussed.
基金the National Natural Science Foundation of China under Grant Nos.61903236,62073202,and 61803240Shandong Provincial National Science Foundation under Grant No.ZR2018BF021China Postdoctoral Science Foundation under Grant No.2017M622262。
文摘This paper studies a class of strategic games,where players often collaborate with other players to form a group when making decisions,and the payoff functions of players in such games are presented as vector functions.First,using the semi-tensor product(STP)method,it is proved that a finite game with vector payoffs is potential if and only if its potential equation has solution.By adding a suitable weight vector to the vector payoffs of each player,a finite game with vector payoffs that is not potential can be converted into a potential game.Second,as a natural generalization,the authors consider the verification problem of the group-based potential games with vector payoffs.By solving a linear potential equation,a simple formula is obtained to calculate the corresponding potential function.Finally,some examples are presented and discussed in detail to illustrate the theoretical results.
基金supported by Future Network Scientific Research Fund Project (FNSRFP-2021-ZD-4)National Natural Science Foundation of China (No.61991404,61902182)+1 种基金National Key Research and Development Program of China under Grant 2020YFB1600104Key Research and Development Plan of Jiangsu Province under Grant BE2020084-2。
文摘With the rapid development of Intelligent Transportation Systems(ITS),many new applications for Intelligent Connected Vehicles(ICVs)have sprung up.In order to tackle the conflict between delay-sensitive applications and resource-constrained vehicles,computation offloading paradigm that transfers computation tasks from ICVs to edge computing nodes has received extensive attention.However,the dynamic network conditions caused by the mobility of vehicles and the unbalanced computing load of edge nodes make ITS face challenges.In this paper,we propose a heterogeneous Vehicular Edge Computing(VEC)architecture with Task Vehicles(TaVs),Service Vehicles(SeVs)and Roadside Units(RSUs),and propose a distributed algorithm,namely PG-MRL,which jointly optimizes offloading decision and resource allocation.In the first stage,the offloading decisions of TaVs are obtained through a potential game.In the second stage,a multi-agent Deep Deterministic Policy Gradient(DDPG),one of deep reinforcement learning algorithms,with centralized training and distributed execution is proposed to optimize the real-time transmission power and subchannel selection.The simulation results show that the proposed PG-MRL algorithm has significant improvements over baseline algorithms in terms of system delay.
基金supported by the National Natural Science Foundation of China under Grant No. 61771488in part by the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province under Grant No. BK20160034+1 种基金 in part by the Open Research Foundation of Science and Technology on Communication Networks Laboratorythe Guang Xi Universities Key Laboratory Fund of Embedded Technology and Intelligent System (Guilin University of Technology)
文摘UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we investigate current works about UAV coverage problem and propose a multi-UAV coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games(EPG) and have Nash equilibrium(NE) points. Then the multi-UAV energy-efficient coverage deployment algorithm based on spatial adaptive play(MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.
基金supported by the National Natural Science Foundation of China under Grant No.61901523 and No.62071488.
文摘This paper investigates the Quality of Experience(QoE)oriented channel access anti-jamming problem in 5th Generation Mobile Communication(5G)ultra-dense networks.Firstly,considering that the 5G base station adopts beamforming technology,an anti-jamming model under Space Division Multiple Access(SDMA)conditions is proposed.Secondly,the confrontational relationship between users and the jammer is formulated as a Stackelberg game.Besides,to achieve global optimization,we design a local cooperation mechanism for users and formulate the cooperation and competition among users as a local altruistic game.By proving that the local altruistic game is an Exact Potential Game(EPG),we further prove the existence of pure strategy Nash Equilibrium(NE)among users and Stackelberg Equilibrium(SE)between users and jammer.Thirdly,to obtain the equilibrium solutions of the proposed games,we propose an anti-jamming channel selection algorithm and improve its convergence speed through heterogeneous learning parameters.The simulation results validate the convergence and effectiveness of the proposed algorithm.Compared with the throughput optimization scheme,our proposed scheme obtain a greater network satisfaction rate.Finally,we also analyze user fairness changes during the algorithm convergence process and get some interesting conclusions.
基金supported by the National Key Research and Development Program of China 2021YFB2900504, 2020YFB1807900。
文摘Low earth orbit(LEO) satellite communications can provide ubiquitous and reliable services,making it an essential part of the Internet of Everything network. Beam hopping(BH) is an emerging technology for effectively addressing the issue of low resource utilization caused by the non-uniform spatio-temporal distribution of traffic demands. However, how to allocate multi-dimensional resources in a timely and efficient way for the highly dynamic LEO satellite systems remains a challenge. This paper proposes a joint beam scheduling and power optimization beam hopping(JBSPO-BH) algorithm considering the differences in the geographic distribution of sink nodes. The JBSPO-BH algorithm decouples the original problem into two sub-problems. The beam scheduling problem is modelled as a potential game,and the Nash equilibrium(NE) point is obtained as the beam scheduling strategy. Moreover, the penalty function interior point method is applied to optimize the power allocation. Simulation results show that the JBSPO-BH algorithm has low time complexity and fast convergence and achieves better performance both in throughput and fairness. Compared with greedybased BH, greedy-based BH with the power optimization, round-robin BH, Max-SINR BH and satellite resource allocation algorithm, the throughput of the proposed algorithm is improved by 44.99%, 20.79%,156.06%, 15.39% and 8.17%, respectively.
基金supported in part by the Jiangsu Provincial Natural Science Foundation for Excellent Young Scholars(Grant No.BK20170089)in part by the National Natural Science Foundation of China(Grant No.61671474)in part by the Jiangsu Provincial Natural Science Fund for Outstanding Young Scholars(Grant No.BK20180028).
文摘Haptic communications is recognized as a promising enabler of extensive services by enabling real-time haptic control and feedback in remote environments,e.g.,teleoperation and autonomous driving.Considering the strict transmission requirements on reliability and latency,Device-to-Device(D2D)communications is introduced to assist haptic communications.In particular,the teleoperators with poor channel quality are assisted by auxiliaries,and each auxiliary and its corresponding teleoperator constitute a D2D pair.However,the haptic interaction and the scarcity of radio resources pose severe challenges to the resource allocation,especially facing the sporadic packet arrivals.First,the contentionbased access scheme is applied to achieve low-latency transmission,where the resource scheduling latency is omitted and users can directly access available resources.In this context,we derive the reliability index of D2D pairs under the contention-based access scheme,i.e.,closed-loop packet error probability.Then,the reliability performance is guaranteed by bidirectional power control,which aims to minimize the sum packet error probability of all D2D pairs.Potential game theory is introduced to solve the problem with low complexity.Accordingly,a distributed power control algorithm based on synchronous log-linear learning is proposed to converge to the optimal Nash Equilibrium.Experimental results demonstrate the superiority of the proposed learning algorithm.
基金partially funded by the National Natural Science Foundation of China(No.62272001).
文摘Mobile Edge Computing(MEC)is a pivotal technology that provides agile-response services by deploying computation and storage resources in proximity to end-users.However,resource-constrained edge servers fall victim to Denial-of-Service(DoS)attacks easily.Failures to mitigate DoS attacks effectively hinder the delivery of reliable and sustainable edge services.Conventional DoS mitigation solutions in cloud computing environments are not directly applicable in MEC environments because their design did not factor in the unique characteristics of MEC environments,e.g.,constrained resources on edge servers and requirements for low service latency.Existing solutions mitigate edge DoS attacks by transferring user requests from edge servers under attacks to others for processing.Furthermore,the heterogeneity in end-users’resource demands can cause resource fragmentation on edge servers and undermine the ability of these solutions to mitigate DoS attacks effectively.User requests often have to be transferred far away for processing,which increases the service latency.To tackle this challenge,this paper presents a fragmentationaware gaming approach called HEDMGame that attempts to minimize service latency by matching user requests to edge servers’remaining resources while making request-transferring decisions.Through theoretical analysis and experimental evaluation,we validate the effectiveness and efficiency of HEDMGame,and demonstrate its superiority over the state-of-the-art solution.
基金supported by the National Key Research and Development Program of China under Grant 2021YFB2900500the Natural Science Foundation for Outstanding Young Scholars of Heilongjiang Province under Grant YQ2020F001the Fundamental Research Funds for the Central Universities under Grant FRFCU 9803503821.
文摘The satellite-terrestrial cooperative network is considered an emerging network architecture,which can adapt to various services and applications in the future communication network.In recent years,the combination of satellite communication and Mobile Edge Computing(MEC)has become an emerging research hotspot.Satellite edge computing can provide users with full coverage on-orbit computing services by deploying MEC servers on satellites.This paper studies the task offloading of multi-user and multi-edge computing satellites and proposes a novel algorithm that joint task offloading and communication computing resource optimization(JTO-CCRO).The JTO-CCRO is decoupled into task offloading and resource allocation sub-problems.After the mutual iteration of the two sub-problems,the system utility function can be further reduced.For the task offloading sub-problem,it is first confirmed that the offloading problem is a game problem.The offloading strategy can be obtained from the Nash equilibrium solution.We confirm resource optimization sub-problem is a convex optimization problem that can be solved by the Lagrange multiplier method.Simulation shows that the JTO-CCRO algorithm can converge quickly and effectively reduce the system utility function.
基金the National Natural Science Foundation of China under Grant No.71971188the Humanity and Social Science Foundation of Ministry of Education of China under Grant No.22YJCZH086+1 种基金the Hebei Natural Science Foundation under Grant Nos.G2022203003 and G2023203008the support Funded by Science Research Project of Hebei Education Department under Grant No.ZD2022142.
文摘With the fast development of Mobile Internet,data traffic generated by end devices is anticipated to witness substantial growth in the future years.However,processing tasks locally will cause latency due to the limited resources of the end devices.Edge-cloud collaboration,an effective solution for latency-sensitive applications,is attracting greater attention from both industry and academia.It combines the advantages of the cloud center with abundant computing resources and edge nodes with low-latency capabilities.In this paper,we propose a two-stage task offloading framework with edge-cloud collaboration to assist end devices processing latency-sensitive tasks either on the edge servers or in the cloud center.As for homogeneous task offloading,in the first stage,the competitive end devices offload tasks to the edge gateways.We formulate the selfish task offloading problem among end devices as a potential game.In the second stage,the edge nodes request resources from the cloud center to process end devices tasks due to their limited resources.Then,we consider the heterogeneous task offloading problem and use intelligent optimization algorithm to obtain the optimal offloading strategy.Simulation results show that the service prices of edge nodes influence the decisions and task offloading costs of end devices.We also verify the intelligent optimization algorithm can achieve optimal performance with low complexity and fast convergence.
基金supported by the National Natural Science Foundation of China (Grant Nos. 62273177, 62020106003 and 62233009)Natural Science Foundation of Jiangsu Province of China (Grant Nos. BK20211566 and 20222012)+2 种基金Programme of Introducing Talents of Discipline to Universities of China (Grant No. B20007)National Key Laboratory of Space Intelligent Control Technology Open Fund (Grant No. HTKJ2023KL502006)Fundamental Research Funds for the Central Universities (Grant No. NI2024001)
文摘This paper proposes a distributed task allocation algorithm based on game theory to solve the complex task allocation optimization problem when UAV clusters carry heterogeneous resources and tasks have heterogeneous demands. Considering the heterogeneity of resources,two pre-processing methods are proposed: one is the grouping algorithm that combines greedy algorithm with simulated annealing algorithm, and the other is the improved K-medoids clustering algorithm based on heterogeneous resources. These pre-process methods, through grouping and clustering, can reduce the complexity of task allocation. The entropy weight method is utilized to prioritize tasks based on multiple metrics. Considering task demands,airborne resources and path cost, a coalition formation game model is established, which is proved to be a potential game. Then a distributed task allocation algorithm based on coalition formation game is designed to address the task allocation problem. Finally, the simulation involving 30 tasks with heterogeneous requirements assigned to 100 UAVs validates the effectiveness of the proposed algorithm, showing that it can achieve good task allocation results with great real-time performance.
基金the National Natural Science Foundation of China(NSFC)under Grant Nos.62073315,61074114,and 61273013。
文摘Nowadays the semi-tensor product(STP)approach to finite games has become a promising new direction.This paper provides a comprehensive survey on this prosperous field.After a brief introduction for STP and finite(networked)games,a description for the principle and fundamental technique of STP approach to finite games is presented.Then several problems and recent results about theory and applications of finite games via STP are presented.A brief comment about the potential use of STP to artificial intelligence is also proposed.
基金This work was supported by the National Natural Science Foundation of China under Grant No.62072209the National Natural Science Foundation of China Youth Fund under Grant No.62002123+2 种基金the Key Research and Development Program of Jilin Province of China under Grant No.20210201082GXthe Scientific and Technological Planning Project of Jilin Province of China under Grant No.JJKH20221010KJthe Development and Reform Commission Project of Jilin Province of China under Grant No.2020C017-2.
文摘Mobile Edge Computing(MEC)has been envisioned as a promising distributed computing paradigm where mobile users offload their tasks to edge nodes to decrease the cost of energy and computation.However,most of the existing studies only consider the congestion of wireless channels as a crucial factor affecting the strategy-making process,while ignoring the impact of offloading among edge nodes.In addition,centralized task offloading strategies result in enormous computation complexity in center nodes.Along this line,we take both the congestion of wireless channels and the offloading among multiple edge nodes into consideration to enrich users'offloading strategies and propose the Parallel User Selection Algorithm(PUS)and Single User Selection Algorithm(SUS)to substantially accelerate the convergence.More practically,we extend the users'offloading strategies to take into account idle devices and cloud services,which considers the potential computing resources at the edge.Furthermore,we construct a potential game in which each user selfishly seeks an optimal strategy to minimize its cost of latency and energy based on acceptable latency,and find the potential function to prove the existence of Nash equilibrium(NE).Additionally,we update PUS to accelerate its convergence and illustrate its performance through the experimental results of three real datasets,and the updated PUS effectively decreases the total cost and reaches Nash equilibrium.
