Semantic channel equalization has emerged as a solution to address language mismatch in multi-user semantic communications. This approach aims to align the latent spaces of an encoder and a decoder which were not jointly trained and it relies on a partition of the semantic (latent) space into atoms based on the the semantic meaning. In this work we explore the role of the semantic space partition in scenarios where the task structure involves a one-to-many mapping between the semantic space and the action space. In such scenarios, partitioning based on hard inference results results in loss of information which degrades the equalization performance. We propose a soft criterion to derive the atoms of the partition which leverages the soft decoder's output and offers a more comprehensive understanding of the semantic space's structure. Through empirical validation, we demonstrate that soft partitioning yields a more descriptive and regular partition of the space, consequently enhancing the performance of the equalization algorithm.

翻译：语义信道均衡已成为解决多用户语义通信中语言不匹配问题的一种方案。该方法旨在对齐未联合训练的编码器与解码器的潜在空间，其核心在于依据语义含义将语义（潜在）空间划分为原子区域。本文探讨了在任务结构涉及语义空间与动作空间之间一对多映射的场景下，语义空间划分的作用。在此类场景中，基于硬推理结果的划分会导致信息损失，从而降低均衡性能。我们提出一种软划分准则来推导划分原子，该准则利用解码器的软输出结果，能够更全面地理解语义空间的结构。通过实证验证，我们证明软划分能产生更具描述性和规则性的空间划分，从而提升均衡算法的性能。