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Latent Space Alignment for Semantic Channel Equalization

Tomás Hüttebräucker, Mohamed Sana, Emilio Calvanese Strinati

TL;DR

The paper addresses semantic communication with language mismatch by modeling it as misalignment between latent semantic partitions and introducing a latent-space alignment approach using a codebook of linear transformations to realign source and target partitions. It proposes two policies for codebook operation, semantic equalization and effectiveness equalization, the latter leveraging task rewards through reinforcement learning. Experiments show that both methods reduce mismatch effects and that the effectiveness policy yields better task performance, approaching the no-mismatch baseline. The framework enables robust distributed task solving under language heterogeneity for dynamic multi-agent systems.

Abstract

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.

Latent Space Alignment for Semantic Channel Equalization

TL;DR

The paper addresses semantic communication with language mismatch by modeling it as misalignment between latent semantic partitions and introducing a latent-space alignment approach using a codebook of linear transformations to realign source and target partitions. It proposes two policies for codebook operation, semantic equalization and effectiveness equalization, the latter leveraging task rewards through reinforcement learning. Experiments show that both methods reduce mismatch effects and that the effectiveness policy yields better task performance, approaching the no-mismatch baseline. The framework enables robust distributed task solving under language heterogeneity for dynamic multi-agent systems.

Abstract

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.
Paper Structure (5 sections, 3 equations, 3 figures)

This paper contains 5 sections, 3 equations, 3 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Latent space alignment
  4. Codebook operation policy
  5. Results and Conclusions

Figures (3)

  • Figure 1: System model.
  • Figure 2: Two languages solving the same task, learned under the same conditions using reinforcement learning, but with different semantic representation.
  • Figure 3: Average episode length (lower is better) for the different communication strategies with varying snr for a stochastic decoder.

Theorems & Definitions (1)

  • Example 1: The scout and the treasure