Distributed Semantic Alignment over Interference Channels: A Game-Theoretic Approach
This addresses semantic coexistence and interference mitigation for AI-driven systems in interference channels, representing an incremental improvement.
The paper tackles the problem of semantic mismatches and interference in AI-driven communication systems by formulating a distributed non-cooperative game for joint optimization of MIMO transceivers, deriving conditions for Nash Equilibrium and demonstrating trade-offs in goal-oriented semantic communication.
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.