Semantic-aware communication is a novel paradigm that draws inspiration from human communication focusing on the delivery of the meaning of messages. It has attracted significant interest recently due to its potential to improve the efficiency and reliability of communication and enhance users' QoE. Most existing works focus on transmitting and delivering the explicit semantic meaning that can be directly identified from the source signal. This paper investigates the implicit semantic-aware communication in which the hidden information that cannot be directly observed from the source signal must be recognized and interpreted by the intended users. To this end, a novel implicit semantic-aware communication (iSAC) architecture is proposed for representing, communicating, and interpreting the implicit semantic meaning between source and destination users. A projection-based semantic encoder is proposed to convert the high-dimensional graphical representation of explicit semantics into a low-dimensional semantic constellation space for efficient physical channel transmission. To enable the destination user to learn and imitate the implicit semantic reasoning process of source user, a generative adversarial imitation learning-based solution, called G-RML, is proposed. Different from existing communication solutions, the source user in G-RML does not focus only on sending as much of the useful messages as possible; but, instead, it tries to guide the destination user to learn a reasoning mechanism to map any observed explicit semantics to the corresponding implicit semantics that are most relevant to the semantic meaning. Compared to the existing solutions, our proposed G-RML requires much less communication and computational resources and scales well to the scenarios involving the communication of rich semantic meanings consisting of a large number of concepts and relations.

翻译：语义感知通信是一种受人类通信启发的新范式，专注于传递消息的意义。因其在提升通信效率与可靠性以及增强用户体验质量方面的潜力，近年来引起了广泛关注。现有工作大多聚焦于传输可直接从源信号中识别的显式语义。本文研究隐式语义感知通信，其中无法直接从源信号观察到的隐藏信息必须由目标用户识别和解释。为此，提出了一种新颖的隐式语义感知通信（iSAC）架构，用于在源用户和目标用户之间表示、传递和解释隐式语义。我们设计了一种基于投影的语义编码器，将显式语义的高维图形表示转换为低维语义星座空间，以实现高效的物理信道传输。为使目标用户能够学习并模仿源用户的隐式语义推理过程，提出了一种基于生成对抗模仿学习的解决方案，称为G-RML。与现有通信方案不同，G-RML中的源用户不仅专注于尽可能多地发送有用信息，而是试图引导目标用户学习一种推理机制，将任何观察到的显式语义映射到与语义含义最相关的对应隐式语义。与现有方案相比，我们提出的G-RML所需的通信和计算资源显著减少，并且能够很好地扩展到涉及包含大量概念和关系的丰富语义含义通信的场景。