Deep learning based joint source-channel coding (JSCC) has demonstrated significant advancements in data reconstruction compared to separate source-channel coding (SSCC). This superiority arises from the suboptimality of SSCC when dealing with finite block-length data. Moreover, SSCC falls short in reconstructing data in a multi-user and/or multi-resolution fashion, as it only tries to satisfy the worst channel and/or the highest quality data. To overcome these limitations, we propose a novel deep learning multi-resolution JSCC framework inspired by the concept of multi-task learning (MTL). This proposed framework excels at encoding data for different resolutions through hierarchical layers and effectively decodes it by leveraging both current and past layers of encoded data. Moreover, this framework holds great potential for semantic communication, where the objective extends beyond data reconstruction to preserving specific semantic attributes throughout the communication process. These semantic features could be crucial elements such as class labels, essential for classification tasks, or other key attributes that require preservation. Within this framework, each level of encoded data can be carefully designed to retain specific data semantics. As a result, the precision of a semantic classifier can be progressively enhanced across successive layers, emphasizing the preservation of targeted semantics throughout the encoding and decoding stages. We conduct experiments on MNIST and CIFAR10 dataset. The experiment with both datasets illustrates that our proposed method is capable of surpassing the SSCC method in reconstructing data with different resolutions, enabling the extraction of semantic features with heightened confidence in successive layers. This capability is particularly advantageous for prioritizing and preserving more crucial semantic features within the datasets.

翻译：基于深度学习的联合信源信道编码（JSCC）在数据重建方面相比传统分离信源信道编码（SSCC）展现出显著优势。这种优越性源于SSCC在处理有限块长数据时的次优性。此外，SSCC在实现多用户和/或多分辨率数据重建方面存在不足，因其仅针对最差信道条件和/或最高数据质量进行优化。为克服这些局限，我们借鉴多任务学习（MTL）理念，提出一种新型深度学习多分辨率JSCC框架。该框架通过分层编码结构高效实现不同分辨率数据的编码，并利用当前及历史编码层数据实现有效解码。该框架在语义通信领域具有巨大潜力，其目标已超越单纯的数据重建，致力于在通信过程中保留特定语义属性。这些语义特征可能是分类任务中关键要素（如类别标签），或其他需保密的属性。在该框架中，各编码层可被精心设计以保留特定数据语义，使得语义分类器的精度随编码层数递增而逐步提升，从而在编解码过程中重点强化目标语义的保留。我们在MNIST和CIFAR10数据集上开展实验。结果表明，所提方法在重建不同分辨率数据方面优于SSCC方法，并能通过逐层递进的方式以更高置信度提取语义特征。该能力对于数据集内关键语义特征的优先保留具有特殊优势。