In this paper, we consider a remote inference system, where a neural network is used to infer a time-varying target (e.g., robot movement), based on features (e.g., video clips) that are progressively received from a sensing node (e.g., a camera). Each feature is a temporal sequence of sensory data. The inference error is determined by (i) the timeliness and (ii) the sequence length of the feature, where we use Age of Information (AoI) as a metric for timeliness. While a longer feature can typically provide better inference performance, it often requires more channel resources for sending the feature. To minimize the time-averaged inference error, we study a learning and communication co-design problem that jointly optimizes feature length selection and transmission scheduling. When there is a single sensor-predictor pair and a single channel, we develop low-complexity optimal co-designs for both the cases of time-invariant and time-variant feature length. When there are multiple sensor-predictor pairs and multiple channels, the co-design problem becomes a restless multi-arm multi-action bandit problem that is PSPACE-hard. For this setting, we design a low-complexity algorithm to solve the problem. Trace-driven evaluations demonstrate the potential of these co-designs to reduce inference error by up to 10000 times.

翻译：本文研究一种远程推理系统，其中神经网络基于从感知节点（例如摄像头）逐步接收的特征（例如视频片段）来推断时变目标（例如机器人运动）。每个特征均为感官数据的时间序列。推理误差由（i）特征的时效性和（ii）序列长度共同决定，其中我们采用信息年龄作为时效性度量指标。虽然更长的特征通常能提供更好的推理性能，但其传输往往需要占用更多信道资源。为最小化时间平均推理误差，我们研究了一个学习与通信协同设计问题，该问题联合优化特征长度选择与传输调度策略。针对单传感器-预测器对与单信道场景，我们分别针对特征长度时不变与时变两种情况提出了低复杂度最优协同设计方案。当存在多传感器-预测器对与多信道时，该协同设计问题转化为一个PSPACE难度的 restless 多臂多动作赌博机问题。针对此场景，我们设计了一种低复杂度算法求解该问题。基于真实数据轨迹的评估表明，所提协同设计方案可将推理误差降低高达10000倍。