联邦潜在空间对齐用于多用户语义通信
Federated Latent Space Alignment for Multi-user Semantic Communications
AI总结：
本文提出联邦潜在空间对齐方法，用于多用户语义通信中的潜在空间对齐，通过共享预均衡器和本地均衡器实现任务导向的高效通信。
摘要：
语义通信旨在通过传达意义来实现有效任务执行，但AI原生设备中不同的潜在表示会导致语义不匹配，从而阻碍相互理解。本文介绍了一种新的方法，用于减轻多代理AI原生语义通信中的潜在空间不匹配问题。在下行链路场景中，我们考虑接入点（AP）与多个用户通信以完成特定的AI驱动任务。我们的方法在AP处实现共享的语义预均衡器，在用户设备处实现本地语义均衡器，促进相互理解和任务导向的通信，同时考虑功率和复杂性约束。为此，我们采用联邦优化来实现AP和用户侧语义均衡器的去中心化训练。数值结果验证了所提出方法在目标导向的语义通信中的有效性，揭示了准确性、通信开销、复杂性和AI原生通信设备语义接近度之间的关键权衡。
英文摘要：
Semantic communication aims to convey meaning for effective task execution, but differing latent representations in AI-native devices can cause semantic mismatches that hinder mutual understanding. This paper introduces a novel approach to mitigating latent space misalignment in multi-agent AI- native semantic communications. In a downlink scenario, we consider an access point (AP) communicating with multiple users to accomplish a specific AI-driven task. Our method implements a protocol that shares a semantic pre-equalizer at the AP and local semantic equalizers at user devices, fostering mutual understanding and task-oriented communication while considering power and complexity constraints. To achieve this, we employ a federated optimization for the decentralized training of the semantic equalizers at the AP and user sides. Numerical results validate the proposed approach in goal-oriented semantic communication, revealing key trade-offs among accuracy, com- munication overhead, complexity, and the semantic proximity of AI-native communication devices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
