Distributed best response dynamics for Nash equilibrium seeking in potential games

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.

Algebraic Verification of Finite Group-Based Potential Games with Vector Payoffs

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.

Potential game design for a class of distributed optimization problems

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.

Anti-jamming channel access in 5G ultra-dense networks: a game-theoretic learning approach

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.

Joint Computing and Communication Resource Allocation for Edge Computing towards Huge LEO Networks

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.

A Comprehensive Survey on STP Approach to Finite Games

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.

Distributed Game-Theoretical D2D-Enabled Task Offloading in Mobile Edge Computing

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.