Joint Optimization of Multipath Routing and Scheduling for Industrial Wireless Deterministic Transmission

CHEN Rongjun; WANG Hongchao; WANG Qinding; QIAO Kai; TIAN Weikang; YANG Dong

doi:10.12263/DZXB.20250734

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Joint Optimization of Multipath Routing and Scheduling for Industrial Wireless Deterministic Transmission

PAPERS | 更新时间：2026-06-04

- Joint Optimization of Multipath Routing and Scheduling for Industrial Wireless Deterministic Transmission
- ACTA ELECTRONICA SINICA Vol. 54, Issue 1, Pages: 68-85(2026)
- 作者机构：
  
  北京交通大学电子信息工程学院，北京 100044
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62425104)
- DOI：10.12263/DZXB.20250734
  CLC： TN913.21;
- Received：21 August 2025，
  
  Accepted：30 December 2025，
  
  Published：25 January 2026
- 稿件说明：
移动端阅览
陈荣均, 王洪超, 王钦定, 等. 面向工业无线确定性传输的多路径路由与调度联合优化[J]. 电子学报, 2026, 54(01): 68-85.

CHEN Rongjun, WANG Hongchao, WANG Qinding, et al. Joint Optimization of Multipath Routing and Scheduling for Industrial Wireless Deterministic Transmission[J]. Acta Electronica Sinica, 2026, 54(01): 68-85.
陈荣均, 王洪超, 王钦定, 等. 面向工业无线确定性传输的多路径路由与调度联合优化[J]. 电子学报, 2026, 54(01): 68-85. DOI：10.12263/DZXB.20250734

CHEN Rongjun, WANG Hongchao, WANG Qinding, et al. Joint Optimization of Multipath Routing and Scheduling for Industrial Wireless Deterministic Transmission[J]. Acta Electronica Sinica, 2026, 54(01): 68-85. DOI：10.12263/DZXB.20250734

摘要

随着工业无线网络和无线通信技术的快速发展，无线网络的确定性传输已成为一个重要的研究方向。然而，无线信道中的不确定因素，如多径衰落和同频干扰，给无线网络的确定性传输带来了诸多挑战。为了解决这些问题，Internet工程任务组（Internet Engineering Task Force，IETF）提出了可靠可用无线（Reliable and Available Wireless，RAW）架构，并在工业无线网络场景中使用时隙跳频（Time-Slotted Channel Hopping，TSCH）作为底层实现技术。为了确保可靠性和严格的时延要求，RAW设计了多种保障机制，包括通过数据包复制、消除与排序功能（Packet Replication， Elimination and Ordering Functions，PREOF）技术利用路径冗余提升传输的可靠性和确定性。然而，现有的调度方案未充分考虑PREOF以及路由和调度的联合优化，导致时频资源分配时存在冗余和资源浪费，从而影响了网络对关键流的调度能力。本文面向确定性流量传输的多路径路由与调度联合优化问题进行建模，并提出了一种基于分层强化学习的资源分配算法（Herarchical Reinforcement Resource Allocation，HRRA）。其中，高层策略负责多路径路由的选择，低层策略则基于高层策略的路由决策进行时频资源的分配，同时考虑PREOF在聚合节点对冗余包的删除。针对拓扑规模的变化和流量的异构性，在高层策略引入图神经网络（Graph Neural Network，GNN）增强对输入特征的表征能力。HRRA算法能够根据流的截止时间、可靠性等需求选择合适的动作，从而最大化调度流数量和资源利用效率。通过这种跨层优化架构和对PREOF的支持，HRRA不仅有效解决了资源冗余和调度能力不足的问题，还增强了对流的确定性通信需求的支持。实验表明，相比于DGRL+MWIS和EDF-MO等基准算法，HRRA分别提升了10.6%和36.6%的调度能力，同时实现了更高的资源利用效率。

Abstract

With the rapid development of industrial wireless networks and wireless communication technologies

deterministic transmission in wireless networks has emerged as an important research direction. However

the inherent uncertainties 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 variety of mechanisms

including the use of packet replication

elimination and ordering functions (PREOF) to exploit path redundancy 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 inefficient 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 resource 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 elimination 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 HRRA algorithm selects appropriate actions according to flow requirements such as deadlines and reliability

thereby maximizing 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.

关键词

Keywords

references

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