Orbital angular momentum(OAM)can achieve multifold increase of spectrum efficiency,but the hollow divergence characteristic and Line-of-Sight(LoS)path requirement impose the crucial challenges for vortex wave communic...Orbital angular momentum(OAM)can achieve multifold increase of spectrum efficiency,but the hollow divergence characteristic and Line-of-Sight(LoS)path requirement impose the crucial challenges for vortex wave communications.For air-to-ground vortex wave communications,where there exists the LoS path,this paper proposes a multi-user cooperative receive(MUCR)scheme to break through the communication distance limitation caused by the characteristic of vortex wave hollow divergence.In particular,we derive the optimal radial position corresponding to the maximum intensity,which is used to adjust the waist radius.Based on the waist radius and energy ring,the cooperative ground users having the minimum angular square difference are selected.Also,the signal compensation scheme is proposed to decompose OAM signals in air-to-ground vortex wave communications.Simulation results are presented to verify the superiority of our proposed MUCR scheme.展开更多
Background The redirected walking(RDW)method for multi-user collaboration requires maintaining the relative position between users in a virtual environment(VE)and physical environment(PE).A chasing game in a VE is a t...Background The redirected walking(RDW)method for multi-user collaboration requires maintaining the relative position between users in a virtual environment(VE)and physical environment(PE).A chasing game in a VE is a typical virtual reality game that entails multi-user collaboration.When a user approaches and interacts with a target user in the VE,the user is expected to approach and interact with the target user in the corresponding PE as well.Existing methods of multi-user RDW mainly focus on obstacle avoidance,which does not account for the relative positional relationship between the users in both VE and PE.Methods To enhance the user experience and facilitate potential interaction,this paper presents a novel dynamic alignment algorithm for multi-user collaborative redirected walking(DA-RDW)in a shared PE where the target user and other users are moving.This algorithm adopts improved artificial potential fields,where the repulsive force is a function of the relative position and velocity of the user with respect to dynamic obstacles.For the best alignment,this algorithm sets the alignment-guidance force in several cases and then converts it into a constrained optimization problem to obtain the optimal direction.Moreover,this algorithm introduces a potential interaction object selection strategy for a dynamically uncertain environment to speed up the subsequent alignment.To balance obstacle avoidance and alignment,this algorithm uses the dynamic weightings of the virtual and physical distances between users and the target to determine the resultant force vector.Results The efficacy of the proposed method was evaluated using a series of simulations and live-user experiments.The experimental results demonstrate that our novel dynamic alignment method for multi-user collaborative redirected walking can reduce the distance error in both VE and PE to improve alignment with fewer collisions.展开更多
To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlin...To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.展开更多
在任务计算密集型和延迟敏感型的场景下,无人机辅助的移动边缘计算由于其高机动性和放置成本低的特点而被广泛研究.然而,无人机的能耗限制导致其无法长时间工作并且卸载任务内的不同模块往往存在着依赖关系.针对这种情况,以有向无环图(d...在任务计算密集型和延迟敏感型的场景下,无人机辅助的移动边缘计算由于其高机动性和放置成本低的特点而被广泛研究.然而,无人机的能耗限制导致其无法长时间工作并且卸载任务内的不同模块往往存在着依赖关系.针对这种情况,以有向无环图(direct acyclic graph,DAG)为基础对任务内部模块的依赖关系进行建模,综合考虑系统时延和能耗的影响,以最小化系统成本为优化目标得到最优的卸载策略.为了解决这一优化问题,提出了一种基于亚群、高斯变异和反向学习的二进制灰狼优化算法(binary grey wolf optimization algorithm based on subpopulation,Gaussian mutation,and reverse learning,BGWOSGR).仿真结果表明,所提出算法计算出的系统成本比其他4种对比方法分别降低了约19%、27%、16%、13%,并且收敛速度更快.展开更多
This paper studies large-scale multi-input multi-output(MIMO)orthogonal frequency division multiplexing(OFDM)communications in a broadband frequency-selective channel,where a massive MIMO base station(BS)communicates ...This paper studies large-scale multi-input multi-output(MIMO)orthogonal frequency division multiplexing(OFDM)communications in a broadband frequency-selective channel,where a massive MIMO base station(BS)communicates with multiple users equipped with multi-antenna.We develop a hybrid precoding design to maximize the weighted sum-rate(WSR)of the users by optimizing the digital and the analog precoders alternately.For the digital part,we employ block-diagonalization to eliminate inter-user interference and apply water-filling power allocation to maximize the WSR.For the analog part,the optimization of the PSN is formulated as an unconstrained problem,which can be efficiently solved by a gradient descent method.Numerical results show that the proposed block-diagonal hybrid precoding algorithm can outperform the existing works.展开更多
This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization pr...This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.展开更多
随着互联网技术的发展,云游戏、虚拟现实和互动直播等新兴交互式多媒体应用引起了广泛关注。当前智能设备的计算能力难以满足多媒体内容对超高渲染和实时交互的需求,且云端赋能方式因存在高带宽、高延迟、高能耗等问题,限制了其在移动...随着互联网技术的发展,云游戏、虚拟现实和互动直播等新兴交互式多媒体应用引起了广泛关注。当前智能设备的计算能力难以满足多媒体内容对超高渲染和实时交互的需求,且云端赋能方式因存在高带宽、高延迟、高能耗等问题,限制了其在移动网络中的实际应用。为应对这些挑战,提出一种边缘计算辅助交互式多媒体应用的系统框架,旨在确保满足用户服务质量需求的前提下降低系统能耗。构建融合非正交多址(Non-Orthogonal Multiple Access,NOMA)与移动边缘计算(Mobile Edge Computing,MEC)技术的网络通信模型,考虑到MEC服务器资源受限以及用户服务质量需求各异等因素,提出联合用户关联和资源分配的优化方案。为高效解决优化问题,结合遗传算法(Genetic Algorithm,GA)和粒子群优化(Particle Swarm Optimization,PSO)的优势,设计了分层自适应搜索算法(Hierarchical GA and PSO Based Adaptive Search Algorithm,HGPASA)。通过一系列仿真实验,充分验证了所提算法的有效性。展开更多
基金supported in part by National Natural Science Foundation of China under Grant 62441115 and 62201427in part by the Ministry of Industry and Information Technology of the People’s Republic of China under Grant CBG01N23-01-04.
文摘Orbital angular momentum(OAM)can achieve multifold increase of spectrum efficiency,but the hollow divergence characteristic and Line-of-Sight(LoS)path requirement impose the crucial challenges for vortex wave communications.For air-to-ground vortex wave communications,where there exists the LoS path,this paper proposes a multi-user cooperative receive(MUCR)scheme to break through the communication distance limitation caused by the characteristic of vortex wave hollow divergence.In particular,we derive the optimal radial position corresponding to the maximum intensity,which is used to adjust the waist radius.Based on the waist radius and energy ring,the cooperative ground users having the minimum angular square difference are selected.Also,the signal compensation scheme is proposed to decompose OAM signals in air-to-ground vortex wave communications.Simulation results are presented to verify the superiority of our proposed MUCR scheme.
基金Supported by STI 2030 Major Projects of China(2021ZD0200400).
文摘Background The redirected walking(RDW)method for multi-user collaboration requires maintaining the relative position between users in a virtual environment(VE)and physical environment(PE).A chasing game in a VE is a typical virtual reality game that entails multi-user collaboration.When a user approaches and interacts with a target user in the VE,the user is expected to approach and interact with the target user in the corresponding PE as well.Existing methods of multi-user RDW mainly focus on obstacle avoidance,which does not account for the relative positional relationship between the users in both VE and PE.Methods To enhance the user experience and facilitate potential interaction,this paper presents a novel dynamic alignment algorithm for multi-user collaborative redirected walking(DA-RDW)in a shared PE where the target user and other users are moving.This algorithm adopts improved artificial potential fields,where the repulsive force is a function of the relative position and velocity of the user with respect to dynamic obstacles.For the best alignment,this algorithm sets the alignment-guidance force in several cases and then converts it into a constrained optimization problem to obtain the optimal direction.Moreover,this algorithm introduces a potential interaction object selection strategy for a dynamically uncertain environment to speed up the subsequent alignment.To balance obstacle avoidance and alignment,this algorithm uses the dynamic weightings of the virtual and physical distances between users and the target to determine the resultant force vector.Results The efficacy of the proposed method was evaluated using a series of simulations and live-user experiments.The experimental results demonstrate that our novel dynamic alignment method for multi-user collaborative redirected walking can reduce the distance error in both VE and PE to improve alignment with fewer collisions.
基金supported by the National Natural Science Foundation of China(No.62071354)the Key Research and Development Program of Shaanxi(No.2022ZDLGY05-08)supported by the ISN State Key Laboratory。
文摘To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.
文摘在任务计算密集型和延迟敏感型的场景下,无人机辅助的移动边缘计算由于其高机动性和放置成本低的特点而被广泛研究.然而,无人机的能耗限制导致其无法长时间工作并且卸载任务内的不同模块往往存在着依赖关系.针对这种情况,以有向无环图(direct acyclic graph,DAG)为基础对任务内部模块的依赖关系进行建模,综合考虑系统时延和能耗的影响,以最小化系统成本为优化目标得到最优的卸载策略.为了解决这一优化问题,提出了一种基于亚群、高斯变异和反向学习的二进制灰狼优化算法(binary grey wolf optimization algorithm based on subpopulation,Gaussian mutation,and reverse learning,BGWOSGR).仿真结果表明,所提出算法计算出的系统成本比其他4种对比方法分别降低了约19%、27%、16%、13%,并且收敛速度更快.
基金supported by National Natural Science Foundation of China(No.61771005)
文摘This paper studies large-scale multi-input multi-output(MIMO)orthogonal frequency division multiplexing(OFDM)communications in a broadband frequency-selective channel,where a massive MIMO base station(BS)communicates with multiple users equipped with multi-antenna.We develop a hybrid precoding design to maximize the weighted sum-rate(WSR)of the users by optimizing the digital and the analog precoders alternately.For the digital part,we employ block-diagonalization to eliminate inter-user interference and apply water-filling power allocation to maximize the WSR.For the analog part,the optimization of the PSN is formulated as an unconstrained problem,which can be efficiently solved by a gradient descent method.Numerical results show that the proposed block-diagonal hybrid precoding algorithm can outperform the existing works.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant 2022JBGP003in part by the National Natural Science Foundation of China(NSFC)under Grant 62071033in part by ZTE IndustryUniversity-Institute Cooperation Funds under Grant No.IA20230217003。
文摘This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.
文摘随着互联网技术的发展,云游戏、虚拟现实和互动直播等新兴交互式多媒体应用引起了广泛关注。当前智能设备的计算能力难以满足多媒体内容对超高渲染和实时交互的需求,且云端赋能方式因存在高带宽、高延迟、高能耗等问题,限制了其在移动网络中的实际应用。为应对这些挑战,提出一种边缘计算辅助交互式多媒体应用的系统框架,旨在确保满足用户服务质量需求的前提下降低系统能耗。构建融合非正交多址(Non-Orthogonal Multiple Access,NOMA)与移动边缘计算(Mobile Edge Computing,MEC)技术的网络通信模型,考虑到MEC服务器资源受限以及用户服务质量需求各异等因素,提出联合用户关联和资源分配的优化方案。为高效解决优化问题,结合遗传算法(Genetic Algorithm,GA)和粒子群优化(Particle Swarm Optimization,PSO)的优势,设计了分层自适应搜索算法(Hierarchical GA and PSO Based Adaptive Search Algorithm,HGPASA)。通过一系列仿真实验,充分验证了所提算法的有效性。
