This paper studies the notion of age in task-oriented communications that aims to execute a task at a receiver utilizing the data at its transmitter. The transmitter-receiver operations are modeled as an encoder-decoder pair that is jointly trained while considering channel effects. The encoder converts data samples into feature vectors of small dimension and transmits them with a small number of channel uses thereby reducing the number of transmissions and latency. Instead of reconstructing input samples, the decoder performs a task, e.g., classification, on the received signals. Applying different deep neural networks of encoder-decoder pairs on MNIST and CIFAR-10 image datasets, the classifier accuracy is shown to increase with the number of channel uses at the expense of longer service time. The peak age of task information (PAoTI) is introduced to analyze this accuracy-latency tradeoff when the age grows unless a received signal is classified correctly. By incorporating channel and traffic effects, design guidelines are obtained for task-oriented communications by characterizing how the PAoTI first decreases and then increases with the number of channel uses. A dynamic update mechanism is presented to adapt the number of channel uses to channel and traffic conditions, and reduce the PAoTI in task-oriented communications.

翻译：本文研究任务导向通信中年龄的概念，旨在利用发射端数据在接收端执行任务。发射端-接收端操作被建模为联合训练的编码器-解码器对，同时考虑信道影响。编码器将数据样本转换为小维度特征向量，并通过少量信道使用进行传输，从而减少传输次数和延迟。解码器无需重构输入样本，而是对接收信号执行分类等任务。通过在MNIST和CIFAR-10图像数据集上应用不同的编码器-解码器深度神经网络，结果表明分类器准确率随信道使用次数增加而提升，但代价是服务时间延长。引入任务信息峰值年龄（PAoTI）来分析这种准确率-延迟权衡，其中当接收信号未被正确分类时年龄会增长。通过结合信道和流量影响，获得了任务导向通信的设计准则，揭示了PAoTI随信道使用次数先减少后增加的特性。提出一种动态更新机制，使信道使用次数适应信道和流量条件，从而降低任务导向通信中的PAoTI。