标题： 无线边缘及时多任务推理的计算和通信协同调度 显示英文标题

标题： Computation and Communication Co-scheduling for Timely Multi-Task Inference at the Wireless Edge

摘要： 在多任务远程推理系统中，智能接收器（例如指挥中心）利用来自多个远程来源（例如边缘传感器）的数据特征执行多种推理任务（例如目标检测）。 促进这些系统及时推理的关键挑战源于：(i) 来源计算能力有限，无法从输入中生成特征；(ii) 信道通信资源有限，无法同时传输特征到接收器。 我们开发了一种新颖的计算与通信协同调度方法，以在满足这些资源限制的情况下最小化推理误差。 具体而言，我们将协同调度问题建模为一个弱耦合马尔可夫决策过程，其中基于信息新旧程度（AoI）衡量推理误差的及时性。 为了克服该问题的PSPACE-hard复杂度，我们分析了其拉格朗日松弛形式，这产生了增益指数，用于评估每个潜在特征生成-传输调度操作对推理误差的改善效果。 基于此，我们开发了一种最大增益优先（MGF）策略，并证明当推理任务数量增加时，该策略渐近最优于原始问题。 实验表明，MGF相比基线策略在不同任务、信道和数据源情况下取得了显著改进。 显示英文摘要

摘要： In multi-task remote inference systems, an intelligent receiver (e.g., command center) performs multiple inference tasks (e.g., target detection) using data features received from several remote sources (e.g., edge sensors). Key challenges to facilitating timely inference in these systems arise from (i) limited computational power of the sources to produce features from their inputs, and (ii) limited communication resources of the channels to carry simultaneous feature transmissions to the receiver. We develop a novel computation and communication co-scheduling methodology which determines feature generation and transmission scheduling to minimize inference errors subject to these resource constraints. Specifically, we formulate the co-scheduling problem as a weakly-coupled Markov decision process with Age of Information (AoI)-based timeliness gauging the inference errors. To overcome its PSPACE-hard complexity, we analyze a Lagrangian relaxation of the problem, which yields gain indices assessing the improvement in inference error for each potential feature generation-transmission scheduling action. Based on this, we develop a maximum gain first (MGF) policy which we show is asymptotically optimal for the original problem as the number of inference tasks increases. Experiments demonstrate that MGF obtains significant improvements over baseline policies for varying tasks, channels, and sources.