In forthcoming AI-assisted 6G networks, integrating semantic, pragmatic, and goal-oriented communication strategies becomes imperative. This integration will enable sensing, transmission, and processing of exclusively pertinent task data, ensuring conveyed information possesses understandable, pragmatic semantic significance, aligning with destination needs and goals. Without doubt, no communication is error free. Within this context, besides errors stemming from typical wireless communication dynamics, potential distortions between transmitter-intended and receiver-interpreted meanings can emerge due to limitations in semantic processing capabilities, as well as language and knowledge representation disparities between transmitters and receivers. The main contribution of this paper is two-fold. First, it proposes and details a novel mathematical modeling of errors stemming from language mismatches at both semantic and effectiveness levels. Second, it provides a novel algorithmic solution to counteract these types of errors which leverages optimal transport theory. Our numerical results show the potential of the proposed mechanism to compensate for language mismatches, thereby enhancing the attainability of reliable communication under noisy communication environments.

翻译：在即将到来的人工智能辅助6G网络中，整合语义、语用及面向目标的通信策略变得至关重要。这种整合将使得仅与任务相关的数据能够被感知、传输和处理，确保所传递的信息具有可理解的、实用的语义意义，并与接收端的需求和目标保持一致。毫无疑问，任何通信都无法完全避免错误。在此背景下，除了源于典型无线通信动态的误差外，由于语义处理能力的限制，以及发送端与接收端之间在语言和知识表示上的差异，发送端意图与接收端解读的含义之间可能出现潜在的失真。本文的主要贡献有两点。首先，它提出并详细阐述了一种新颖的数学模型，用于刻画源于语义和效用层面语言不匹配的误差。其次，它提供了一种新颖的算法解决方案来对抗这类误差，该方案利用了最优传输理论。我们的数值结果表明，所提出的机制在补偿语言不匹配方面具有潜力，从而提高了在噪声通信环境下实现可靠通信的可能性。