6G网络的新范式：主动信道认知与重构

中文摘要 AI 辅助

第六代（6G）无线网络有望在高度动态的环境中实现通信、感知、计算、控制与智能的深度融合。这一演进推动了从传统被动信道适配到主动信道认知与重构的根本性转变，在此范式下，无线信道不再被视为不可控的传播介质，而是可被学习、预测与主动重构的网络资源。本文对这一新兴范式进行了全面综述。首先，我们通过信道知识图谱（CKM）这一系统框架综述信道认知，该框架用于学习和利用空间、时间及频域的信道特性，全面综述了CKM的定义、构建方法及其在无线网络中的应用。基于信道认知，我们从两个互补视角综述信道重构技术：由可移动天线（MAs）实现的收发器侧重构，以及由智能反射表面（IRSs）实现的环境侧重构。对于支持MA和IRS的无线系统，我们综述了其架构、性能优势及关键设计挑战。最后，我们探讨了几个有前景的研究方向，以激发这一新兴领域的进一步创新。

英文摘要

The sixth-generation (6G) wireless networks are expected to enable the deep integration of communication, sensing, computing, control, and intelligence in highly dynamic environments. This evolution drives a fundamental transition from conventional passive channel adaptation to proactive channel cognition and reconfiguration, wherein wireless channels are no longer regarded as uncontrollable propagation media but as network resources that can be learned, predicted, and actively reconfigured. This paper presents a comprehensive overview of this emerging paradigm. We first review channel cognition through the channel knowledge map (CKM) as a systematic framework for learning and exploiting channel characteristics across space, time, and frequency domain. The definitions, construction methods, and applications in wireless networks of CKMs are comprehensively reviewed. Building upon channel cognition, we then review channel reconfiguration technologies from two complementary perspectives: transceiver-side reconfiguration enabled by movable antennas (MAs) and environment-side reconfiguration enabled by intelligent reflecting surfaces (IRSs). For both MA- and IRS-enabled wireless systems, we review their architectures, performance advantages, and key design challenges. Finally, we discuss several promising research directions to inspire further innovations in this burgeoning field.

发表机构

• National University of Singapore（新加坡国立大学）
• Beijing Institute of Technology（北京理工大学）
• Beihang University（北京航空航天大学）

机构由 AI 辅助整理，请以论文原文为准。