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面向环境感知无线网络的通用CKM:实现跨设备与跨任务的信道知识迁移

Universal CKM for Environment-Aware Wireless Networks: Enabling Cross-Device and Cross-Task Channel Knowledge Transfer

Haiquan Lu, Yong Zeng, Cheng-Xiang Wang, Xiqi Gao, Rui Zhang

arXiv 2608.17382首次发表:更新:

发表机构

Nanjing University of Science and Technology; National Mobile Communications Research Laboratory, Southeast University; Purple Mountain Laboratories; Department of Electrical and Computer Engineering, National University of Singapore(南京理工大学; 东南大学移动通信国家重点实验室; 紫金山实验室; 新加坡国立大学电气与计算机工程系)

机构由 AI 辅助整理,请以论文原文为准。

AI 中文总结

本文针对现有CKM与设备、任务耦合的局限,提出通用CKM(uCKM)概念,构建跨设备跨任务信道知识迁移新范式,经仿真验证其可行性与性能增益。

AI 中文摘要

信道知识地图(CKM)是面向环境感知第六代(6G)无线网络的极具潜力的技术。然而,现有大多数CKM与无线设备及下游任务紧密耦合,限制了其在无线网络中的可扩展性与可复用性。为解决这些局限,本文提出通用CKM(uCKM)的概念,将其作为基础无线环境先验,旨在为环境感知无线网络实现跨设备与跨任务的信道知识迁移。我们首先回顾代表性CKM并探讨其局限;接着介绍uCKM赋能的环境感知无线网络新范式,从uCKM构建与利用阶段阐述其优势,提出“所有资源服务于uCKM”与“uCKM服务于所有场景”的愿景,即所有设备与任务采集的数据均应助力uCKM构建,反之亦然;随后讨论uCKM面临的主要挑战并提出潜在解决方案;最后通过仿真结果验证uCKM的可行性与性能增益,并展望未来研究方向。

英文摘要

Channel knowledge map (CKM) is a promising technology for environment-aware sixth-generation (6G) wireless networks. However, existing CKMs are tightly coupled with wireless devices and downstream tasks, which limit their scalability and reusability in wireless networks. To address these limitations, this article proposes the concept of universal CKM (uCKM) as a foundational wireless environment prior, which aims to enable cross-device and cross-task channel knowledge transfer for environment-aware wireless networks. We first revisit the representative CKMs and discuss their limitations. Then, the uCKM-enabled new paradigm for environment-aware wireless networks is introduced, and its benefits are highlighted from the perspectives of uCKM construction and utilization phases, for which we propose the visions of ``All for uCKM'' and ``uCKM for All'', i.e., the data acquired by all devices and tasks should contribute to the construction of uCKM, while the constructed uCKM, in turn, can be utilized to support all devices and tasks. Subsequently, we discuss the main challenges of uCKM and propose potential solutions. Last, we provide simulation results to demonstrate the feasibility and performance gains brought by uCKM and outline future research directions.

论文原文

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