Task-oriented communications, mostly using learning-based joint source-channel coding (JSCC), aim to design a communication-efficient edge inference system by transmitting task-relevant information to the receiver. However, only transmitting task-relevant information without introducing any redundancy may cause robustness issues in learning due to the channel variations, and the JSCC which directly maps the source data into continuous channel input symbols poses compatibility issues on existing digital communication systems. In this paper, we address these two issues by first investigating the inherent tradeoff between the informativeness of the encoded representations and the robustness to information distortion in the received representations, and then propose a task-oriented communication scheme with digital modulation, named discrete task-oriented JSCC (DT-JSCC), where the transmitter encodes the features into a discrete representation and transmits it to the receiver with the digital modulation scheme. In the DT-JSCC scheme, we develop a robust encoding framework, named robust information bottleneck (RIB), to improve the communication robustness to the channel variations, and derive a tractable variational upper bound of the RIB objective function using the variational approximation to overcome the computational intractability of mutual information. The experimental results demonstrate that the proposed DT-JSCC achieves better inference performance than the baseline methods with low communication latency, and exhibits robustness to channel variations due to the applied RIB framework.

翻译：任务导向通信主要采用基于学习的联合信源信道编码（JSCC），旨在通过传输任务相关信息至接收端，构建通信高效的边缘推理系统。然而，仅传输任务相关信息而不引入冗余，可能因信道变化导致学习中的鲁棒性问题；同时，JSCC直接将源数据映射为连续信道输入符号，对现有数字通信系统存在兼容性问题。本文针对这两个问题，首先探究编码表示的信息性与接收表示对信息失真的鲁棒性之间的内在权衡，进而提出一种基于数字调制的任务导向通信方案，命名为离散任务导向联合信源信道编码（DT-JSCC）。该方案中，发送端将特征编码为离散表示，并通过数字调制方案传输至接收端。在DT-JSCC框架下，我们开发了一种鲁棒编码框架——鲁棒信息瓶颈（RIB），以提升对信道变化的通信鲁棒性，并通过变分近似推导出RIB目标函数的易处理变分上界，以克服互信息计算不可行的问题。实验结果表明，所提出的DT-JSCC在低通信延迟下实现了优于基线方法的推理性能，并因采用RIB框架展现出对信道变化的鲁棒性。