Soft Partitioning of Latent Space for Semantic Channel Equalization

TL;DR

This paper introduces a soft partitioning method for semantic space in multi-user semantic communication, improving channel equalization by better capturing the space's structure and handling one-to-many mappings.

Contribution

It proposes a novel soft partitioning criterion based on decoder outputs, enhancing semantic space understanding and equalization performance.

Findings

Soft partitioning improves semantic space representation.

Enhanced equalization performance demonstrated empirically.

More descriptive and regular partitions achieved.

Abstract

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.