Semantic communications enable AI-native wireless systems by mapping raw data into compressed task-oriented latent representations. However, independently trained agents often rely on heterogeneous latent spaces and background knowledge, leading to semantic mismatch that degrades mutual understanding and downstream task execution, especially in interferencelimited multi-user wireless networks. This paper investigates distributed latent-space alignment in multi-user semantic MIMO interference networks with cognitive radio constraints. We consider primary users and semantic-aware secondary users sharing the same wireless resources, where secondary agents must simultaneously mitigate interference and align heterogeneous semantic representations. To address this problem, we formulate semantic alignment as a non-cooperative game and derive a closed-form solution for the joint optimization of linear semantic MIMO transceivers under power and interference constraints. Exploiting the structure of the problem, we recast the original matrix valued optimization into a lower-dimensional power-allocation game, leading to an iterative semantic water-filling algorithm. We establish sufficient conditions for existence, uniqueness, and global convergence to a Nash equilibrium, explicitly relating semantic alignment properties and physical-channel interactions. Numerical results assess the performance of the proposed framework, revealing key trade-offs among semantic compression, task performance, and hierarchical spectrum access.

翻译：语义通信通过将原始数据压缩成任务导向的紧凑隐表示，赋能AI原生无线系统。然而，独立训练的智能体通常依赖异构隐空间和背景知识，导致语义失配，从而降低相互理解与下游任务执行能力，尤其在干扰受限的多用户无线网络中。本文研究认知无线电约束下多用户语义MIMO干扰网络中的分布式隐空间对齐问题。考虑主用户和语义感知次用户共享相同无线资源，其中次用户智能体需同时缓解干扰并对齐异构语义表示。针对该问题，我们将语义对齐建模为非合作博弈，并在功率与干扰约束下推导出线性语义MIMO收发机联合优化的闭式解。利用问题结构，我们将原始矩阵值优化重构为低维功率分配博弈，进而提出迭代语义注水算法。我们建立了纳什均衡存在性、唯一性及全局收敛的充分条件，明确关联了语义对齐特性与物理信道交互。数值结果评估了所提框架的性能，揭示了语义压缩、任务性能与分层频谱接入之间的关键权衡。