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Distributed Semantic Alignment over Interference Channels: A Game-Theoretic Approach

Giuseppe Di Poce, Mattia Merluzzi, Emilio Calvanese Strinati, Paolo Di Lorenzo

TL;DR

This paper forms the joint optimization of linear Multiple-Input-Multiple-Output (MIMO) transceivers as a distributed non-cooperative game, enabling a closed-form solution that effectively addresses semantic coexistence and latent space misalignment.

Abstract

Semantic communication acts as a key enabler for effective task execution in AI-driven systems, prioritizing the extraction of the underlying meaning before transmission. However, when devices rely on different logic and internal representations, semantic mismatches may arise, potentially hindering mutual understanding and effectiveness of communication. Furthermore, in interference channel environments, the coexistence of multiple devices introduce a significant degradation due to the presence of multi-user-interference. To address these challenges, in this paper we formulate the joint optimization of linear Multiple-Input-Multiple-Output (MIMO) transceivers as a distributed non-cooperative game, enabling a closed-form solution that effectively addresses semantic coexistence and latent space misalignment. We derive sufficient conditions for the existence of a Nash Equilibrium (NE), considering multiple point-to-point MIMO channels, with corresponding users modeled as selfish players optimizing their transmission and semantic alignment strategies. Numerical results substantiate the proposed approach in goal-oriented semantic communication by highlighting crucial trade-offs between information compression, interference mitigation, semantic alignment, and task performance.

Distributed Semantic Alignment over Interference Channels: A Game-Theoretic Approach

TL;DR

This paper forms the joint optimization of linear Multiple-Input-Multiple-Output (MIMO) transceivers as a distributed non-cooperative game, enabling a closed-form solution that effectively addresses semantic coexistence and latent space misalignment.

Abstract

Semantic communication acts as a key enabler for effective task execution in AI-driven systems, prioritizing the extraction of the underlying meaning before transmission. However, when devices rely on different logic and internal representations, semantic mismatches may arise, potentially hindering mutual understanding and effectiveness of communication. Furthermore, in interference channel environments, the coexistence of multiple devices introduce a significant degradation due to the presence of multi-user-interference. To address these challenges, in this paper we formulate the joint optimization of linear Multiple-Input-Multiple-Output (MIMO) transceivers as a distributed non-cooperative game, enabling a closed-form solution that effectively addresses semantic coexistence and latent space misalignment. We derive sufficient conditions for the existence of a Nash Equilibrium (NE), considering multiple point-to-point MIMO channels, with corresponding users modeled as selfish players optimizing their transmission and semantic alignment strategies. Numerical results substantiate the proposed approach in goal-oriented semantic communication by highlighting crucial trade-offs between information compression, interference mitigation, semantic alignment, and task performance.
Paper Structure (7 sections, 19 equations, 5 figures, 1 algorithm)

This paper contains 7 sections, 19 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Game-theoretic Semantic Equalization
  4. Optimal Semantic Pre-equalizer
  5. Distributed Games for Semantic Equalization
  6. Numerical Results
  7. Conclusions

Figures (5)

  • Figure 1: Overview of the proposed goal-oriented system model.
  • Figure 2: System BR-iterative behavior with $N_{T_l},N_{R_l}\!=\!8$, $K\!=\!10$, $\alpha\!=\!3$. Network (left) and per player (right) $\text{MSE}_l(\boldsymbol{\Phi}_l)$ over game iterations: vit_small_patch16 is the tx deployed in the middle of the system, suffering the most MUI.
  • Figure 3: System BR-iterative behavior with $N_{T_l},N_{R_l}\!=\!8$, $K\!=\!10$, $\alpha\!=\!3$. MUI power in dB (left) and network down-stream task accuracy (right) over game iterations.
  • Figure 4: Accuracy-MSE vs. $\xi$, with $N_{T_l}\! =\! N_{R_l} \!=\! 8$ and $\alpha\!=\!3$.
  • Figure 5: Network Accuracy vs. $\alpha$ (MUI scaling factor), with $N_{T_l} = N_{R_l} \!=\! 4,8$ and wireless channel usage $K\!=\!10$.