Unmanned Aerial Vehicles(UAVs)enabled Aerial Base Stations(UABSs)have been studied widely in future communications.However,there are a series of challenges such as interference management,trajectory design and resourc...Unmanned Aerial Vehicles(UAVs)enabled Aerial Base Stations(UABSs)have been studied widely in future communications.However,there are a series of challenges such as interference management,trajectory design and resource allocation in the scenarios of multi-UAV networks.Besides,different performances among UABSs increase complexity and bring many challenges.In this paper,the joint downlink transmission power control and trajectory design problem in multi-type UABSs communication network is investigated.In order to satisfy the signal to interference plus noise power ratio of users,each UABS needs to adjust its position and transmission power.Based on the interactions among multiple communication links,a non-cooperative Mean-Field-Type Game(MFTG)is proposed to model the joint optimization problem.Then,a Nash equilibrium solution is solved by two steps:first,the users in the given area are clustered to get the initial deployment of the UABSs;second,the Mean-Field Q(MFQ)-learning algorithm is proposed to solve the discrete MFTG problem.Finally,the effectiveness of the approach is verified through the simulations,which simplifies the solution process and effectively reduces the energy consumption of each UABS.展开更多
This paper considers a mean-field type stochastic control problem where the dynamics is governed by a forward and backward stochastic differential equation (SDE) driven by Lévy processes and the information avail...This paper considers a mean-field type stochastic control problem where the dynamics is governed by a forward and backward stochastic differential equation (SDE) driven by Lévy processes and the information available to the controller is possibly less than the overall information. All the system coefficients and the objective performance functional are allowed to be random, possibly non-Markovian. Malliavin calculus is employed to derive a maximum principle for the optimal control of such a system where the adjoint process is explicitly expressed.展开更多
This paper reviews the mean field social(MFS)optimal control problem for multi-agent dynamic systems and the mean-field-type(MFT)optimal control problem for single-agent dynamic systems within the linear quadratic(LQ)...This paper reviews the mean field social(MFS)optimal control problem for multi-agent dynamic systems and the mean-field-type(MFT)optimal control problem for single-agent dynamic systems within the linear quadratic(LQ)framework.For the MFS control problem,this review discusses the existing conclusions on optimization in dynamic systems affected by both additive and multiplicative noises.In exploring MFT optimization,the authors first revisit researches associated with single-player systems constrained by these dynamics.The authors then extend the proposed review to scenarios that include multiple players engaged in Nash games,Stackelberg games,and cooperative Pareto games.Finally,the paper concludes by emphasizing future research on intelligent algorithms for mean field optimization,particularly using reinforcement learning method to design strategies for models with unknown parameters.展开更多
This paper solves a mean-field type hierarchical optimal control problem in electricity market.The authors consider n-1 prosumers and one producer.The ith prosumer,for 1<i<n,is a leader of the(i-1)th prosumer an...This paper solves a mean-field type hierarchical optimal control problem in electricity market.The authors consider n-1 prosumers and one producer.The ith prosumer,for 1<i<n,is a leader of the(i-1)th prosumer and is a follower of the(i+1)th prosumer.The first player(agent)is the follower at the bottom whereas the nth is the leader at the top.The problem is described by a linear jump-diffusion system of conditional mean-field type,where the conditioning is with respect to common noise,and a quadratic cost functional involving,the square of the conditional expectation of the controls of the agents.The authors provide a semi-explicit solution of the corresponding meanfield-type hierarchical control problem with common noise.Finally,the authors illustrate the obtained result via a numerical example with two different scenarios.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.62001387,61901379)the Natural Science Basic Research Plan in Shaanxi Province(No.2019JQ253)+4 种基金the Key R&D Plan of Shaanxi Province(No.2020GY034)the Aerospace Science and Technology Innovation Fund of China Aerospace Science and Technology Corporationthe Shanghai Aerospace Science and Technology Innovation Fund(No.SAST2018045)the China Fundamental Research Fund for the Central Universities(No.3102018QD096)the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University(No.CX2020152)。
文摘Unmanned Aerial Vehicles(UAVs)enabled Aerial Base Stations(UABSs)have been studied widely in future communications.However,there are a series of challenges such as interference management,trajectory design and resource allocation in the scenarios of multi-UAV networks.Besides,different performances among UABSs increase complexity and bring many challenges.In this paper,the joint downlink transmission power control and trajectory design problem in multi-type UABSs communication network is investigated.In order to satisfy the signal to interference plus noise power ratio of users,each UABS needs to adjust its position and transmission power.Based on the interactions among multiple communication links,a non-cooperative Mean-Field-Type Game(MFTG)is proposed to model the joint optimization problem.Then,a Nash equilibrium solution is solved by two steps:first,the users in the given area are clustered to get the initial deployment of the UABSs;second,the Mean-Field Q(MFQ)-learning algorithm is proposed to solve the discrete MFTG problem.Finally,the effectiveness of the approach is verified through the simulations,which simplifies the solution process and effectively reduces the energy consumption of each UABS.
文摘This paper considers a mean-field type stochastic control problem where the dynamics is governed by a forward and backward stochastic differential equation (SDE) driven by Lévy processes and the information available to the controller is possibly less than the overall information. All the system coefficients and the objective performance functional are allowed to be random, possibly non-Markovian. Malliavin calculus is employed to derive a maximum principle for the optimal control of such a system where the adjoint process is explicitly expressed.
基金supported by the National Natural Science Foundation of China under Grant Nos.62103442,12326343,62373229the Research Grants Council of the Hong Kong Special Administrative Region,China under Grant Nos.CityU 11213023,11205724+3 种基金the Natural Science Foundation of Shandong Province under Grant No.ZR2021QF080the Taishan Scholar Project of Shandong Province under Grant No.tsqn202408110the Fundamental Research Foundation of the Central Universities under Grant No.23CX06024Athe Outstanding Youth Innovation Team in Shandong Higher Education Institutions under Grant No.2023KJ061.
文摘This paper reviews the mean field social(MFS)optimal control problem for multi-agent dynamic systems and the mean-field-type(MFT)optimal control problem for single-agent dynamic systems within the linear quadratic(LQ)framework.For the MFS control problem,this review discusses the existing conclusions on optimization in dynamic systems affected by both additive and multiplicative noises.In exploring MFT optimization,the authors first revisit researches associated with single-player systems constrained by these dynamics.The authors then extend the proposed review to scenarios that include multiple players engaged in Nash games,Stackelberg games,and cooperative Pareto games.Finally,the paper concludes by emphasizing future research on intelligent algorithms for mean field optimization,particularly using reinforcement learning method to design strategies for models with unknown parameters.
基金support from Tamkeen under the NYU Abu Dhabi Research Institute grant CG002U.S.Air Force Office of Scientific Research under Grant No.FA955017-1-0259。
文摘This paper solves a mean-field type hierarchical optimal control problem in electricity market.The authors consider n-1 prosumers and one producer.The ith prosumer,for 1<i<n,is a leader of the(i-1)th prosumer and is a follower of the(i+1)th prosumer.The first player(agent)is the follower at the bottom whereas the nth is the leader at the top.The problem is described by a linear jump-diffusion system of conditional mean-field type,where the conditioning is with respect to common noise,and a quadratic cost functional involving,the square of the conditional expectation of the controls of the agents.The authors provide a semi-explicit solution of the corresponding meanfield-type hierarchical control problem with common noise.Finally,the authors illustrate the obtained result via a numerical example with two different scenarios.
