# Soft Partitioning of Latent Space for Semantic Channel Equalization

arXiv: https://arxiv.org/abs/2405.20085 · HTML: https://mlsanity.com/paper/2405.20085/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 7 of 7)

- [Relative representations enable zero-shot latent space communication](https://mlsanity.com/paper/2209.15430/) (2022)
- [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020)
- [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020)
- [Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach](https://mlsanity.com/paper/2402.07573/) (2024)
- [Learning Semantics: An Opportunity for Effective 6G Communications](https://mlsanity.com/paper/2110.08049/) (2021)
- [Semantic Channel Equalizer: Modelling Language Mismatch in Multi-User Semantic Communications](https://mlsanity.com/paper/2308.03789/) (2023)
- [Pragmatic Goal-Oriented Communications under Semantic-Effectiveness Channel Errors](https://mlsanity.com/paper/2402.16858/) (2024)

## Built on by (most cited first, 3 of 3)

- [Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization](https://mlsanity.com/paper/2411.19719/) (2024)
- [Distillation-Enabled Knowledge Alignment Protocol for Semantic Communication in AI Agent Networks](https://mlsanity.com/paper/2505.17030/) (2025)
- [RIS-aided Latent Space Alignment for Semantic Channel Equalization](https://mlsanity.com/paper/2507.16450/) (2025)

## Abstract (arXiv)

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.