With the recent advancements in edge artificial intelligence (AI), future sixth-generation (6G) networks need to support new AI tasks such as classification and clustering apart from data recovery. Motivated by the success of deep learning, the semantic-aware and task-oriented communications with deep joint source and channel coding (JSCC) have emerged as new paradigm shifts in 6G from the conventional data-oriented communications with separate source and channel coding (SSCC). However, most existing works focused on the deep JSCC designs for one task of data recovery or AI task execution independently, which cannot be transferred to other unintended tasks. Differently, this paper investigates the JSCC semantic communications to support multi-task services, by performing the image data recovery and classification task execution simultaneously. First, we propose a new end-to-end deep JSCC framework by unifying the coding rate reduction maximization and the mean square error (MSE) minimization in the loss function. Here, the coding rate reduction maximization facilitates the learning of discriminative features for enabling to perform classification tasks directly in the feature space, and the MSE minimization helps the learning of informative features for high-quality image data recovery. Next, to further improve the robustness against variational wireless channels, we propose a new gated deep JSCC design, in which a gated net is incorporated for adaptively pruning the output features to adjust their dimensions based on channel conditions. Finally, we present extensive numerical experiments to validate the performance of our proposed deep JSCC designs as compared to various benchmark schemes.

翻译：随着边缘人工智能的最新进展，未来第六代（6G）网络除了支持数据恢复外，还需支撑分类、聚类等新型AI任务。受深度学习成功经验启发，采用深度联合源信道编码（JSCC）的语义感知与任务导向通信，正成为6G中从传统基于分离源信道编码（SSCC）的数据导向通信范式转变的新方向。然而，现有研究大多聚焦于独立优化数据恢复或AI任务执行某一类任务的深度JSCC设计，此类方案无法迁移至其他非目标任务。本文则另辟蹊径，通过同时实现图像数据恢复与分类任务执行，研究支持多任务服务的JSCC语义通信。首先，我们提出一种新型端到端深度JSCC框架，在损失函数中统一融合编码率缩减最大化（编码率缩减最大化）与均方误差（MSE）最小化。其中，编码率缩减最大化有助于学习判别性特征，使特征空间可直接执行分类任务；而MSE最小化则促进信息性特征学习，支撑高质量图像数据恢复。其次，为提升对时变无线信道的鲁棒性，我们提出新的门控深度JSCC设计，引入门控网络自适应剪枝输出特征，根据信道条件调整特征维度。最后，通过大量数值实验验证了所提深度JSCC方案相较于多种基准方案的性能优势。