文献类型：期刊文献

英文题名：Multi-Link Enabled Reliable and Low Latency Transmission Design for Edge Estimation

作者：Qian, Siyu[1];Wang, Xiaolin[1,2];Li, Fangfei[1];Ren, Yi'ang[1]

机构：[1]East China Univ Sci & Technol, Sch Math, Shanghai 200237, Peoples R China;[2]Minist Educ, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China

年份：2026

卷号：13

起止页码：4707

外文期刊名：IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING

收录：;EI(收录号：20255119734728);WOS:【SCI-EXPANDED(收录号:WOS:001649694200015)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62573198, Grant 62173142, Grant 62233005, Grant 62303185, and Grant U2441245, in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, and in part by Shanghai Sailing Program under Grant 23YF1409500.

语种：英文

外文关键词：Estimation; Reliability; Optimization; Industrial Internet of Things; Throughput; Dynamic scheduling; Wireless sensor networks; Sensors; Resource management; Job shop scheduling; Scheduling and estimation co-design; multi-link operation; hierarchical deep reinforcement learning

摘要：Edge computing enables real-time estimation for mission-critical Industrial Internet of Things (IIoT) systems by processing massive data streams at the network edge. Existing methods struggle to balance estimation performance and energy efficiency under varying reliability and latency requirements. We investigate Multi-Link Operation (MLO) to improve adaptability under dynamic wireless conditions. However, existing schemes offer limited flexibility in transmission modes and rely on simplified network models, which restricts the potential for MLO-specific performance optimization. To overcome these challenges, we propose a novel flexible transmission mode selection mechanism called Synchronous-Asynchronous Co-existence Multi-Link Aggregation (SACMLA). Based on switchable MLO modes, we formulate a scheduling-estimation co-design problem that optimizes the trade-off between estimation error covariance and transmission energy consumption. Given the environmental complexity and the action space explosion caused by the joint optimization of transmission mode, packet-to-link mapping, and energy allocation, we design a Proximal Policy Optimization (PPO)-based Hierarchical Deep Reinforcement Learning for Multi-Link Co-optimization (HDRL-MLC), a two-tier architecture where a PPO-based outer loop dynamically adjusts scheduling, and an inner loop optimizes energy allocation in MLO. Simulation results demonstrate the necessity of the SACMLA mechanism and highlight the superior performance of the PPO-based HDRL-MLC algorithm over flat algorithms in balancing multiple communication metrics and adapting to varying traffic conditions.