Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-toler...Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-tolerant (SDT) traffic, we design a dynamic switching strategy based on a traffic-Qo S-aware soft slicing (TQASS) scheme and a resource-efficiency-aware soft slicing (REASS) scheme.展开更多
The coexistence of ultra-reliable low-latency communication(URLLC)and enhanced mobile broadband(eMBB)services in 5G-based industrial wireless networks(IWNs)poses significant resource slicing challenges due to their in...The coexistence of ultra-reliable low-latency communication(URLLC)and enhanced mobile broadband(eMBB)services in 5G-based industrial wireless networks(IWNs)poses significant resource slicing challenges due to their inherent performance requirement conflicts.To address this challenge,this paper proposes a puncturing method that uses a model-aided deep reinforcement learning(DRL)algorithm for URLLC over eMBB services in uplink 5G networks.First,a puncturing-based optimization problem is formulated to maximize the eMBB accumulated rate under strict URLLC latency and reliability constraints.Next,we design a random repetition coding-based contention(RRCC)scheme for sporadic URLLC traffic and derive its analytical reliability model.To jointly optimize the scheduling parameters of URLLC and eMBB,a DRL solution based on the reliability model is developed,which is capable of dynamically adapting to changing environments.The accelerated convergence of the model-aided DRL algorithm is demonstrated using simulations,and the superiority in resource efficiency of the proposed method over existing approaches is validated.展开更多
基金supported by the Liaoning Revitalization Talents Program(XLYC2203148)
文摘Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-tolerant (SDT) traffic, we design a dynamic switching strategy based on a traffic-Qo S-aware soft slicing (TQASS) scheme and a resource-efficiency-aware soft slicing (REASS) scheme.
基金Project supported by the Liaoning Revitalization Talents Program(Nos.XLYC2203148 and XLYC2403062)the National Natural Science Foundation of China(Nos.92267108 and 62173322)。
文摘The coexistence of ultra-reliable low-latency communication(URLLC)and enhanced mobile broadband(eMBB)services in 5G-based industrial wireless networks(IWNs)poses significant resource slicing challenges due to their inherent performance requirement conflicts.To address this challenge,this paper proposes a puncturing method that uses a model-aided deep reinforcement learning(DRL)algorithm for URLLC over eMBB services in uplink 5G networks.First,a puncturing-based optimization problem is formulated to maximize the eMBB accumulated rate under strict URLLC latency and reliability constraints.Next,we design a random repetition coding-based contention(RRCC)scheme for sporadic URLLC traffic and derive its analytical reliability model.To jointly optimize the scheduling parameters of URLLC and eMBB,a DRL solution based on the reliability model is developed,which is capable of dynamically adapting to changing environments.The accelerated convergence of the model-aided DRL algorithm is demonstrated using simulations,and the superiority in resource efficiency of the proposed method over existing approaches is validated.
