针对工业物联网时延敏感数据，优化任务卸载与资源分配以提升信息新鲜度。
AoI-Aware Task Offloading and Transmission Optimization for Industrial IoT Networks: A Branching Deep Reinforcement Learning Approach
- 设计分支结构D3QN算法，将多基站决策复杂度从指数降至线性。
- 实现75%更快收敛速度，长期平均信息年龄降低至少22%。
- 适合需要实时监控与高可靠通信的智能制造场景。
在工业互联网（IIoT）中，无线网络频繁传输大量数据需满足严格时延要求。尤其，数据包状态更新的新鲜度对系统性能影响显著。本文提出一种面向信息年龄（AoI）的多基站实时监控框架，支持大规模IIoT部署。为满足时延要求，构建联合任务卸载与资源分配优化问题，目标是最小化长期平均AoI。解决多基站决策空间组合爆炸及系统随机动态带来的挑战至关重要，传统方法因此难以应用。首先，提出基于分支的双延迟深度Q网络（Branching-D3QN）算法，通过减少动作空间复杂度，有效实现任务卸载，显著提升收敛性能。其次，通过证明带宽与计算资源的海森矩阵半正定性，提出高效的资源分配优化方案。最后，设计迭代优化算法，实现任务卸载与资源分配的联合优化，达到最优平均AoI。大量仿真表明，所提Branching-D3QN算法优于现有先进DRL方法与经典启发式算法，收敛速度提升高达75%，长期平均AoI降低至少22%。
原文摘要 · Abstract (English)
In the Industrial Internet of Things (IIoT), the frequent transmission of large amounts of data over wireless networks should meet the stringent timeliness requirements. Particularly, the freshness of packet status updates has a significant impact on the system performance. In this paper, we propose an age-of-information (AoI)-aware multi-base station (BS) real-time monitoring framework to support extensive IIoT deployments. To meet the freshness requirements of IIoT, we formulate a joint task offloading and resource allocation optimization problem with the goal of minimizing long-term average AoI. Tackling the core challenges of combinatorial explosion in multi-BS decision spaces and the stochastic dynamics of IIoT systems is crucial, as these factors render traditional optimization methods intractable. Firstly, an innovative branching-based Dueling Double Deep Q-Network (Branching-D3QN) algorithm is proposed to effectively implement task offloading, which optimizes the convergence performance by reducing the action space complexity from exponential to linear levels. Then, an efficient optimization solution to resource allocation is proposed by proving the semi-definite property of the Hessian matrix of bandwidth and computation resources. Finally, we propose an iterative optimization algorithm for efficient joint task offloading and resource allocation to achieve optimal average AoI performance. Extensive simulations demonstrate that our proposed Branching-D3QN algorithm outperforms both state-of-the-art DRL methods and classical heuristics, achieving up to a 75% enhanced convergence speed and at least a 22% reduction in the long-term average AoI.
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