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.

通过软划分语义空间，改进语义信道均衡算法的性能。

语义频道均衡的隐变量空间软分区

Soft Partitioning of Latent Space for Semantic Channel Equalization

We relax the constraint of a shared language between agents in a semantic and
goal-oriented communication system to explore the effect of language mismatch
in distributed task solving. We propose a mathematical framework, which
provides a modelling and a measure of the semantic distortion introduced in the
communication when agents use distinct languages. We then propose a new
approach to semantic channel equalization with proven effectiveness through
numerical evaluations.

我们通过一个语义和目标导向的通信系统，放宽了代理之间的共享语言的约束，以探索在分布式任务求解中语言不匹配的影响。我们提出了一个数学框架，模拟并度量了代理使用不同语言时引入的语义失真，并通过数值评估证明了一种新的语义通道均衡方法的有效性。