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.

在即将到来的 AI 辅助的 6G 网络中，整合语义、语用和目标导向的沟通策略变得必不可少。本文的主要贡献是：首先，提出并详细说明了一种新的数学建模方法，用于描述由于语义和有效性水平上的语言不匹配而导致的错误；其次，提供了一种利用最优传输理论来抵消这些类型错误的新算法解决方案。我们的数值结果显示了所提出机制在嘈杂的通信环境下弥补语言不匹配的潜力，从而提高了可靠通信的可达性。

语义有效性通道误差下的务实目标导向通信

Pragmatic Goal-Oriented Communications under Semantic-Effectiveness  Channel Errors

Polar codes are widely used state-of-the-art codes for reliable communication
that have recently been included in the 5th generation wireless standards (5G).
However, there remains room for the design of polar decoders that are both
efficient and reliable in the short blocklength regime. Motivated by recent
successes of data-driven channel decoders, we introduce a novel
$\textbf{C}$ur$\textbf{RI}$culum based $\textbf{S}$equential neural decoder for
$\textbf{P}$olar codes (CRISP). We design a principled curriculum, guided by
information-theoretic insights, to train CRISP and show that it outperforms the
successive-cancellation (SC) decoder and attains near-optimal reliability
performance on the Polar(32,16) and Polar(64,22) codes. The choice of the
proposed curriculum is critical in achieving the accuracy gains of CRISP, as we
show by comparing against other curricula. More notably, CRISP can be readily
extended to Polarization-Adjusted-Convolutional (PAC) codes, where existing SC
decoders are significantly less reliable. To the best of our knowledge, CRISP
constructs the first data-driven decoder for PAC codes and attains near-optimal
performance on the PAC(32,16) code.

本文提出一种基于课程的顺序神经网络解码器（CRISP）用于极化码，该解码器基于信息论指导下的有原则的课程设计，使用数据驱动策略训练网络，通过与其他课程的比较，CRISP 在 Polar（32,16）和 Polar（64,22）码上表现出色，在现有 SC 解码器可靠性较低的情况下，CRISP 可轻松扩展到极化调整卷积（PAC）码，并且成为第一个在 PAC（32,16）码上表现近乎最优的数据驱动解码器。