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Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization

Tomás Hüttebräucker, Simone Fiorellino, Mohamed Sana, Paolo Di Lorenzo, Emilio Calvanese Strinati

TL;DR

The paper tackles semantic mismatch in multi-user semantic communications by introducing a zero-shot semantic channel equalization framework based on relative representations. Latent alignment is achieved via a common relative space defined by an anchor set ${\mathbb A}$, with the equalizer implemented as $T_{\theta\to\gamma}=R_\gamma^{-1}\circ R_\theta$, and a novel prototypical anchors approach selected through clustering to maximize task-relevant information. Experimental results on Tiny-ImageNet across diverse encoders demonstrate robust cross-model communication, with performance improving as the number of anchors grows and prototypical anchors outperform random ones; the method is agnostic to the similarity function $\psi$ and offers compression via anchor-based representation. A key finding is that better semantic alignment does not always guarantee GO performance, underscoring the distinct roles of semantic interpretation and downstream goals, and suggesting that stronger encoders at the transmitter can mitigate limitations of the receiver.

Abstract

In multi-user semantic communication, language mismatche poses a significant challenge when independently trained agents interact. We present a novel semantic equalization algorithm that enables communication between agents with different languages without additional retraining. Our algorithm is based on relative representations, a framework that enables different agents employing different neural network models to have unified representation. It proceeds by projecting the latent vectors of different models into a common space defined relative to a set of data samples called \textit{anchors}, whose number equals the dimension of the resulting space. A communication between different agents translates to a communication of semantic symbols sampled from this relative space. This approach, in addition to aligning the semantic representations of different agents, allows compressing the amount of information being exchanged, by appropriately selecting the number of anchors. Eventually, we introduce a novel anchor selection strategy, which advantageously determines prototypical anchors, capturing the most relevant information for the downstream task. Our numerical results show the effectiveness of the proposed approach allowing seamless communication between agents with radically different models, including differences in terms of neural network architecture and datasets used for initial training.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization

TL;DR

The paper tackles semantic mismatch in multi-user semantic communications by introducing a zero-shot semantic channel equalization framework based on relative representations. Latent alignment is achieved via a common relative space defined by an anchor set , with the equalizer implemented as , and a novel prototypical anchors approach selected through clustering to maximize task-relevant information. Experimental results on Tiny-ImageNet across diverse encoders demonstrate robust cross-model communication, with performance improving as the number of anchors grows and prototypical anchors outperform random ones; the method is agnostic to the similarity function and offers compression via anchor-based representation. A key finding is that better semantic alignment does not always guarantee GO performance, underscoring the distinct roles of semantic interpretation and downstream goals, and suggesting that stronger encoders at the transmitter can mitigate limitations of the receiver.

Abstract

In multi-user semantic communication, language mismatche poses a significant challenge when independently trained agents interact. We present a novel semantic equalization algorithm that enables communication between agents with different languages without additional retraining. Our algorithm is based on relative representations, a framework that enables different agents employing different neural network models to have unified representation. It proceeds by projecting the latent vectors of different models into a common space defined relative to a set of data samples called \textit{anchors}, whose number equals the dimension of the resulting space. A communication between different agents translates to a communication of semantic symbols sampled from this relative space. This approach, in addition to aligning the semantic representations of different agents, allows compressing the amount of information being exchanged, by appropriately selecting the number of anchors. Eventually, we introduce a novel anchor selection strategy, which advantageously determines prototypical anchors, capturing the most relevant information for the downstream task. Our numerical results show the effectiveness of the proposed approach allowing seamless communication between agents with radically different models, including differences in terms of neural network architecture and datasets used for initial training.

Paper Structure

This paper contains 11 sections, 10 equations, 3 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Semantic Channel Equalization
  4. Relative Representations
  5. Proposed Solution
  6. Relative Projection Inverse $R_{\gamma}^{-1}$
  7. Prototypical Anchors
  8. Results
  9. Equalization performance
  10. Semantic vs Goal oriented equalization performance
  11. Conclusions and Future Work

Figures (3)

  • Figure 1: System model showing the language mismatch and our proposed solution to create a common communication channel between independently trained models.
  • Figure 2: Performance of the proposed Semantic Channel Equalization algorithm (two leftmost columns) and the closed-form inverse used in maiorca2024latent (rightmost column). Results are compared with a random anchor selection and the proposed prototypical anchors.
  • Figure 3: Accuracy as a function of the error in the reconstruction of the target semantic symbol ${\mathbf z}_{\gamma}$ for the Semantic Channel Equalization method. The number of anchors used is shown in the marker size, bigger marker size corresponds to a higher number of anchors used.