Soft Partitioning of Latent Space for Semantic Channel Equalization

TL;DR

The paper addresses semantic mismatch in multi-user semantic communications and its impact on semantic channel equalization by examining how latent-space partitioning affects performance. It argues that hard partitioning, which ties atoms to single actions, can be suboptimal in the presence of action ambiguities, and proposes soft partitioning based on clustering action-value representations to form latent-space atoms. Using a grid-world RL setup with RL-based encoders/decoders and a codebook of linear transformations, it demonstrates that soft partitioning yields more regular atoms and can improve downstream task performance, especially when the number of atoms is chosen appropriately. The findings suggest that richer semantic descriptions facilitate multi-task equalization in heterogeneous agent networks and point to future work extending SEC to larger action spaces and multitask scenarios.

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.

Figures (7)

• Figure 1: Proposed communication scenario shared with huttebraucker2024pragmatic. A distributed control problem is explored, where the language of the encoder does not match the language of the decoder. Using a codebook of transformations and a selection policy, semantic channel equalization is performed.
• Figure 2: Two possible partitions for the latent space of a language generator. The generator was trained with the task of MNIST classification. Different ways to partition the semantic space depend on the criteria used (digit classification or digit parity classification)
• Figure 3: Different ways to partition the semantic space capture different semantics. When using the hard decision outcome to define the partition, the structure of the task and the relationship between actions is lost. When using the soft decision values, all the task-relevant information is exploited for the partition.
• Figure 4: Projection of the action-value space of dimension $n_a=4$ into the first two data maximum variance directions for the source language. Each point corresponds to an observation. Colors are shown according to the action that maximized the value for each observation.
• Figure 5: Different partitions of the semantic space using the k-means algorithm with varying number of atoms. The actions (hard partitioning) are visualized as different shapes for each partition. The color of the points corresponds to a given atom and the actions associated with each atom are shown next to it in the color legend.