Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become pivotal issue in semantic communications. This paper proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data oriented semantic communications (JTD-SC) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion. First, by analyzing the Bayesian model of the DSSCC framework, we derive a novel rate-distortion optimization problem via the Bayesian inference approach for general data distributions and semantic tasks. Next, for a typical application of joint image transmission and classification, we combine the variational autoencoder approach with a forward adaption scheme to effectively extract image features and adaptively learn the density information of the obtained features. Finally, an iterative training algorithm is proposed to tackle the overfitting issue of deep learning models. Simulation results reveal that the proposed scheme achieves better coding gain as well as data recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes.

翻译：语义通信通过利用源数据的语义特征，有望在消耗较少频谱资源的情况下完成各类语义任务。为同时兼顾数据传输与语义任务，数据压缩与语义分析的联合已成为语义通信中的关键问题。本文针对面向任务与数据协同的语义通信（JTD-SC），提出了一种深度分离信源信道编码（DSSCC）框架，并采用变分自编码器方法解决包含语义失真的率失真问题。首先，通过分析DSSCC框架的贝叶斯模型，我们基于贝叶斯推断方法推导出适用于一般数据分布与语义任务的新型率失真优化问题。其次，针对图像联合传输与分类的典型应用，我们将变分自编码器方法与前向自适应方案相结合，以有效提取图像特征并自适应学习所获特征的密度信息。最后，提出一种迭代训练算法以应对深度学习模型的过拟合问题。仿真结果表明，与经典压缩方案及新兴的深度联合信源信道方案相比，所提方案在大多数场景下能实现更优的编码增益、数据恢复与分类性能。