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.

翻译：语义通信作为人工智能驱动系统中高效任务执行的关键赋能技术，其核心在于传输前优先提取信息的深层含义。然而，当设备依赖不同的逻辑与内部表征时，可能产生语义失配，进而阻碍通信的相互理解与有效性。此外，在干扰信道环境中，多设备共存会因多用户干扰的存在而导致性能显著下降。为应对这些挑战，本文将线性多输入多输出收发机的联合优化建模为一个分布式非合作博弈，从而获得能够有效处理语义共存与潜在空间失配问题的闭式解。我们推导了纳什均衡存在的充分条件，其中考虑多个点对点多输入多输出信道，并将对应用户建模为优化其传输与语义对齐策略的自私参与者。数值结果通过揭示信息压缩、干扰抑制、语义对齐与任务性能之间的关键权衡关系，验证了所提方法在面向目标的语义通信中的有效性。