Multidomain Adaptive Semantic Communications

Dongwook Won;Quang Tuan Do;Thwe Thwe Win;Donghyun Lee;Junsuk Oh;Sungrae Cho
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Abstract

The domain adaptation issues in semantic communications become critical when transmitter and receiver operate across different multiple domains or when input data during inference have different distributional characteristics than the data used to train semantic encoders and decoders. In this paper, we introduce the Multidomain Adaptive Deep Semantic Communication (MA-DeepSC) framework, designed to enhance semantic communications across multiple domains. Our framework consists of two core components: the Multidomain Adaptive Semantic Coding Network (MASCN), inherently designed to adapt semantic encoding and decoding across multiple domains, and the multidomain data adaptation network (MDAN), which transforms actual observable data into the data on which the system was initially trained, thus obviating the need for retraining the existing pre-trained semantic coding network. We validate our approach through experiments on digit datasets and CelebA, observing significant outperformance over existing techniques. In addition, we analyze the strategic benefits and drawbacks of both MASC and MDAN, assessing their applicability under various scenarios. The source code for MA-DeepSC is available at https://github.com/wongdongwook/JSAC_MA-DeepSC
多域自适应语义通信
当发送端和接收端在不同的多个域上运行,或者当推理过程中的输入数据与用于训练语义编码器和解码器的数据具有不同的分布特征时,语义通信中的领域自适应问题变得至关重要。在本文中,我们引入了多域自适应深度语义通信(MA-DeepSC)框架,旨在增强跨多域的语义通信。我们的框架由两个核心组件组成:多域自适应语义编码网络(MASCN)和多域数据适应网络(MDAN),多域自适应语义编码网络本质上是为了适应跨多个域的语义编码和解码,多域数据适应网络(MDAN)将实际可观察数据转换为系统最初训练的数据,从而避免了对现有预训练的语义编码网络进行重新训练的需要。我们通过数字数据集和CelebA的实验验证了我们的方法,观察到比现有技术有显著的优势。此外,我们分析了MASC和MDAN的战略优势和劣势,评估了它们在不同场景下的适用性。MA-DeepSC的源代码可在https://github.com/wongdongwook/JSAC_MA-DeepSC获得
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