Semantic communication is a novel communication paradigm that focuses on recognizing and delivering the desired meaning of messages to the destination users. Most existing works in this area focus on delivering explicit semantics, labels or signal features that can be directly identified from the source signals. In this paper, we consider the implicit semantic communication problem in which hidden relations and closely related semantic terms that cannot be recognized from the source signals need to also be delivered to the destination user. We develop a novel adversarial learning-based implicit semantic-aware communication (iSAC) architecture in which the source user, instead of maximizing the total amount of information transmitted to the channel, aims to help the recipient learn an inference rule that can automatically generate implicit semantics based on limited clue information. We prove that by applying iSAC, the destination user can always learn an inference rule that matches the true inference rule of the source messages. Experimental results show that the proposed iSAC can offer up to a 19.69 dB improvement over existing non-inferential communication solutions, in terms of symbol error rate at the destination user.

翻译：语义通信是一种新型通信范式，专注于识别并传递消息的预期含义至目标用户。现有研究多聚焦于显式语义、标签或可直接从源信号中识别的信号特征的传递。本文考虑隐式语义通信问题，即需将无法从源信号直接识别的隐藏关联及紧密相关的语义术语一并传递至目标用户。我们提出一种基于对抗学习的隐式语义感知通信（iSAC）架构，其中源用户并非最大化信道传输信息总量，而是旨在协助接收方学习一种推理规则，使其能够基于有限线索信息自动生成隐式语义。理论上证明，应用iSAC后，目标用户总能学习到与源消息真实推理规则相匹配的推理规则。实验结果表明，在目标用户处的符号错误率方面，所提出的iSAC相较于现有非推理通信方案可实现高达19.69 dB的性能提升。