We study the design of goal-oriented communication strategies for remote inference, where an inferrer (e.g., a trained neural network) on the receiver side predicts a time-varying target signal (e.g., the position of the car in front) using the data packet (e.g., video clip) most recently received from a sensor (e.g., camera). The communication between the sensor and the receiver is carried out over a two-way channel. The objective is to minimize the expected inference error per time slot by exploiting the memory of the delay in the channel. It turns out that the optimal policy is an index-based threshold policy. The scheduler submits a packet at suitable time slots for which the index function exceeds a threshold. The index function depends on the current age of information on the receiver side and the prior knowledge about the delay in the subsequent packet transmission.

翻译：我们研究面向远程推理的目标导向通信策略设计，其中接收端的推理器（例如，预训练的神经网络）利用传感器（例如，摄像头）最新发送的数据包（例如，视频片段）预测时变目标信号（例如，前车位置）。传感器与接收端之间的通信通过双向信道进行。目标是利用信道延迟的记忆特性，最小化每个时隙的期望推理误差。结果表明，最优策略为基于索引的阈值策略。调度器在索引函数超过阈值的合适时隙提交数据包。该索引函数取决于接收端当前的年龄信息以及后续数据包传输延迟的先验知识。