发表机构
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST); Kyung Hee University; Munster Technological University（国家科学技术大学 电气工程与计算机科学学院; 庆熙大学; 芒斯特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对AI原生6G网络中异构AI模型导致的信念对齐难题，提出基于MEC服务器潜在翻译模型的异质性感知信念同步框架，可降低同步成本、保护隐私并维持低信念对齐误差。
AI 中文摘要
6G网络将不再仅作为通信基础设施，而是有望演变为互联数千个自主人工智能（AI）智能体的智能系统。这些智能体部署在低轨（LEO）卫星、高空平台（HAP）、无人机（UAV）、边缘服务器及地面设备等各类平台上，持续观测环境并交换信息。语义通信提供了一种高效机制，用于交换有意义的信息而非原始数据，但其有效性取决于通信智能体具有足够一致的信念以正确解释和解码传输的消息。在6G网络中，异构AI模型在不同计算约束下运行，且持续从本地环境获取不同知识，这使得上述假设难以满足。本文提出一种面向6G AI原生网络的异质性感知信念同步框架，该框架采用部署在多接入边缘计算（MEC）服务器上的潜在翻译模型，将一个智能体的信念更新转换为特定于另一智能体知识的内容，无需联合训练及模型的同构架构。该框架仅在必要时通过潜在翻译模型交换紧凑的信念更新，从而保护隐私、降低同步成本并最小化本地知识漂移。我们通过对多层地面/非地面网络的案例研究验证了该框架，结果表明其在案例研究中维持了以传输参数数量衡量的低同步成本，以及异构智能体间的低信念对齐误差。
英文摘要
6G networks will not be serving as communication infrastructures only; rather, they are expected to evolve into intelligent systems, where thousands of autonomous artificial intelligence (AI) agents are interconnected. The agents are deployed across a wide range of platforms including low Earth orbit (LEO) satellites, high-altitude platforms (HAPs), unmanned aerial vehicles (UAVs), edge servers, and terrestrial devices. These agents continuously observe their environment and exchange information. Semantic communication provides an efficient mechanism for exchanging meaningful information instead of raw data. However, its effectiveness depends on the communicating agents having sufficiently aligned beliefs to correctly interpret and decode the transmitted messages. This assumption becomes difficult to satisfy in the 6G network where heterogeneous AI models operate under diverse computational constraints and continuously acquire different knowledge from their local environments. This article presents a heterogeneity-aware belief synchronization framework for 6G AI-native networks. It uses latent translation models deployed on multi-access edge computing (MEC) servers. These models translate belief updates from one agent to agent-specific knowledge without requiring joint training and a homogeneous architecture of models. By exchanging compact belief updates through a latent translation model only when necessary, the framework preserves privacy, reduces synchronization cost, and minimizes local knowledge drift. We validate the framework through a case study on a multi-layered terrestrial/non-terrestrial network. Results demonstrate that it maintains low synchronization cost, measured by the number of parameters transmitted, and low belief alignment error across the heterogeneous agents in the case study.
Comments8 pages, 4 figures
