面向工业无线确定性传输的多路径路由与调度联合优化OA

With the rapid development of industrial wireless networks and wireless communication technologies,de-terministic transmission in wireless networks has emerged as an important research direction.However,the inherent uncer-tainties of wireless channels,such as multipath fading and co-channel interference,pose significant challenges to achieving deterministic transmission.To address these challenges,the internet engineering task force(IETF)proposed the reliable and available wireless(RAW)architecture,which adopts time-slotted channel hopping(TSCH)as the underlying technology in industrial wireless network scenarios.In order to ensure reliability and stringent delay requirements,RAW incorporates a va-riety of mechanisms,including the use of packet replication,elimination and ordering functions(PREOF)to exploit path re-dundancy and thereby enhance transmission reliability and determinism.Nevertheless,existing scheduling schemes have not sufficiently considered PREOF or the joint optimization of routing and scheduling.This results in redundancy and ineffi-cient resource allocation in the time-frequency domain,limiting the network's ability to support critical flows.In this work,we formulate the joint optimization problem of multipath routing and scheduling for deterministic flow transmission and propose a hierarchical reinforcement learning-based resource allocation algorithm,termed hierarchical reinforcement re-source allocation(HRRA).In HRRA,the high-level policy is responsible for selecting multipath routes,while the low-level policy allocates time-frequency resources based on the high-level routing decisions,explicitly accounting for the elimina-tion of redundant packets by PREOF at aggregation nodes.To address variations in topology size and heterogeneous traffic demands,a graph neural network(GNN)is integrated into the high-level policy to enhance feature representation.The HR-RA algorithm selects appropriate actions according to flow requirements such as deadlines and reliability,thereby maximiz-ing both the number of schedulable flows and overall resource utilization.Through this cross-layer optimization framework and explicit support for PREOF,HRRA not only mitigates redundancy and improves scheduling efficiency but also better supports deterministic communication requirements.Experimental results demonstrate that,compared to baseline schemes such as DGRL+MWIS and EDF-MO,HRRA improves scheduling capability by 10.6%and 36.6%,respectively,while achieving higher resource utilization.