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

TL;DR

This paper introduces a game-theoretic framework for optimizing semantic communication over interference channels, enabling devices to align semantics and mitigate interference through distributed MIMO transceiver strategies.

Contribution

It formulates a non-cooperative game for joint semantic and interference optimization in MIMO systems, providing a closed-form solution and conditions for Nash Equilibrium.

Findings

Effective semantic coexistence achieved

Trade-offs between compression, interference, and task performance identified

Numerical results validate the proposed game-theoretic approach

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.