Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably incr...Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably increases computing demands,risking real-time system stability.Vehicle Edge Computing(VEC)addresses this by offloading tasks to Road Side Units(RSUs),ensuring timely services.Our previous work,the FLSimCo algorithm,which uses local resources for federated Self-Supervised Learning(SSL),has a limitation:vehicles often can’t complete all iteration tasks.Our improved algorithm offloads partial tasks to RSUs and optimizes energy consumption by adjusting transmission power,CPU frequency,and task assignment ratios,balancing local and RSU-based training.Meanwhile,setting an offloading threshold further prevents inefficiencies.Simulation results show that the enhanced algorithm reduces energy consumption and improves offloading efficiency and accuracy of federated SSL.展开更多
针对飞行自组网(Flying Ad Hoc Network,FANET)在通信空白场景下存在的高时延问题,提出了一种深度强化学习(Deep Reinforcement Learning,DRL)辅助的双跳信息增强路由协议(Double-Hop Information Enhanced Routing Protocol,DHRP)。为...针对飞行自组网(Flying Ad Hoc Network,FANET)在通信空白场景下存在的高时延问题,提出了一种深度强化学习(Deep Reinforcement Learning,DRL)辅助的双跳信息增强路由协议(Double-Hop Information Enhanced Routing Protocol,DHRP)。为了实现有效的路由决策,采用马尔可夫决策过程(Markov Decision Process,MDP)对路由行为进行建模,在状态空间设计中结合了节点位置信息与链路信道容量,并综合考虑了双跳范围内的网络信息,以深度值网络为核心,在融合实时网络状态动态调整机制的奖励函数引导下,做出最优下一跳路由决策。实验结果表明,在通信空白场景下,DHRP相较于现有的路由方案,显著降低了FANET的平均端到端时延。此外,在不同节点规模和网络拥塞条件下,DHRP均表现出优越的适应性和鲁棒性,通过对动态网络环境的实时感知与智能决策机制,有效保障了整体网络性能。展开更多
The emergence of beyond 5G networks has the potential for seamless and intelligent connectivity on a global scale.Network slicing is crucial in delivering services for different,demanding vertical applications in this...The emergence of beyond 5G networks has the potential for seamless and intelligent connectivity on a global scale.Network slicing is crucial in delivering services for different,demanding vertical applications in this context.Next-generation applications have time-sensitive requirements and depend on the most efficient routing path to ensure packets reach their intended destinations.However,the existing IP(Internet Protocol)over a multi-domain network faces challenges in enforcing network slicing due to minimal collaboration and information sharing among network operators.Conventional inter-domain routing methods,like Border Gateway Protocol(BGP),cannot make routing decisions based on performance,which frequently results in traffic flowing across congested paths that are never optimal.To address these issues,we propose CoopAI-Route,a multi-agent cooperative deep reinforcement learning(DRL)system utilizing hierarchical software-defined networks(SDN).This framework enforces network slicing in multi-domain networks and cooperative communication with various administrators to find performance-based routes in intra-and inter-domain.CoopAI-Route employs the Distributed Global Topology(DGT)algorithm to define inter-domain Quality of Service(QoS)paths.CoopAI-Route uses a DRL agent with a message-passing multi-agent Twin-Delayed Deep Deterministic Policy Gradient method to ensure optimal end-to-end routes adapted to the specific requirements of network slicing applications.Our evaluation demonstrates CoopAI-Route’s commendable performance in scalability,link failure handling,and adaptability to evolving topologies compared to state-of-the-art methods.展开更多
文摘Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably increases computing demands,risking real-time system stability.Vehicle Edge Computing(VEC)addresses this by offloading tasks to Road Side Units(RSUs),ensuring timely services.Our previous work,the FLSimCo algorithm,which uses local resources for federated Self-Supervised Learning(SSL),has a limitation:vehicles often can’t complete all iteration tasks.Our improved algorithm offloads partial tasks to RSUs and optimizes energy consumption by adjusting transmission power,CPU frequency,and task assignment ratios,balancing local and RSU-based training.Meanwhile,setting an offloading threshold further prevents inefficiencies.Simulation results show that the enhanced algorithm reduces energy consumption and improves offloading efficiency and accuracy of federated SSL.
文摘针对飞行自组网(Flying Ad Hoc Network,FANET)在通信空白场景下存在的高时延问题,提出了一种深度强化学习(Deep Reinforcement Learning,DRL)辅助的双跳信息增强路由协议(Double-Hop Information Enhanced Routing Protocol,DHRP)。为了实现有效的路由决策,采用马尔可夫决策过程(Markov Decision Process,MDP)对路由行为进行建模,在状态空间设计中结合了节点位置信息与链路信道容量,并综合考虑了双跳范围内的网络信息,以深度值网络为核心,在融合实时网络状态动态调整机制的奖励函数引导下,做出最优下一跳路由决策。实验结果表明,在通信空白场景下,DHRP相较于现有的路由方案,显著降低了FANET的平均端到端时延。此外,在不同节点规模和网络拥塞条件下,DHRP均表现出优越的适应性和鲁棒性,通过对动态网络环境的实时感知与智能决策机制,有效保障了整体网络性能。
文摘The emergence of beyond 5G networks has the potential for seamless and intelligent connectivity on a global scale.Network slicing is crucial in delivering services for different,demanding vertical applications in this context.Next-generation applications have time-sensitive requirements and depend on the most efficient routing path to ensure packets reach their intended destinations.However,the existing IP(Internet Protocol)over a multi-domain network faces challenges in enforcing network slicing due to minimal collaboration and information sharing among network operators.Conventional inter-domain routing methods,like Border Gateway Protocol(BGP),cannot make routing decisions based on performance,which frequently results in traffic flowing across congested paths that are never optimal.To address these issues,we propose CoopAI-Route,a multi-agent cooperative deep reinforcement learning(DRL)system utilizing hierarchical software-defined networks(SDN).This framework enforces network slicing in multi-domain networks and cooperative communication with various administrators to find performance-based routes in intra-and inter-domain.CoopAI-Route employs the Distributed Global Topology(DGT)algorithm to define inter-domain Quality of Service(QoS)paths.CoopAI-Route uses a DRL agent with a message-passing multi-agent Twin-Delayed Deep Deterministic Policy Gradient method to ensure optimal end-to-end routes adapted to the specific requirements of network slicing applications.Our evaluation demonstrates CoopAI-Route’s commendable performance in scalability,link failure handling,and adaptability to evolving topologies compared to state-of-the-art methods.
