Dynamic Channel Knowledge Map: Fundamentals, Construction, and Applications

2026-07-19

AI Summary

This work addresses the limitations of conventional channel knowledge graphs (CKGs), which capture only static environments and thus struggle to model time-varying channels induced by dynamic scatterers, terminal orientation changes, and radio-frequency impairments—leading to prohibitively high overhead in acquiring high-dimensional channel state information. To overcome this, the paper proposes a Dynamic Channel Knowledge Graph (Dynamic CKG), establishing for the first time a systematic theoretical framework that serves as an intermediate representation layer bridging static environmental priors and physical-layer signal processing. This framework enables joint pilot design, interference mitigation, and integrated sensing and communication. By integrating geospatial data, time-varying channel modeling, and machine learning–driven graph construction, the approach achieves co-design of CKG and signal processing, significantly reducing channel acquisition overhead while enhancing both communication efficiency and sensing performance, thereby offering a novel paradigm for 6G systems.

Abstract

Wireless communication networks are evolving toward extremely large antenna arrays, millimeter-wave and terahertz bands, and dense heterogeneous deployments, all of which increase channel dimensionality and make channel acquisition increasingly costly. Channel knowledge map (CKM) establishes a mapping from geographical locations to channel characteristics, providing location-specific prior information to reduce the overhead of channel acquisition. Most existing CKM research, however, has focused on quasi-static propagation features shaped by quasi-static environmental structures such as buildings and terrain, leaving unaddressed the time-varying channel component introduced by dynamic scatterers, terminal attitude changes, and radio-frequency (RF) impairments. This article presents a new concept of dynamic CKM as a middle layer that links quasi-static environmental priors to physical-layer signal processing by providing time-evolving channel representations. We first introduce the fundamentals of dynamic CKM, clarifying its relationship with the quasi-static CKM and the physical layer. We then survey representative construction methods and discuss how dynamic CKM can support pilot design, interference suppression, and integrated sensing and communications. Finally, we outline key open research directions in the co-design of dynamic CKM construction and physical-layer signal processing. These discussions offer an architectural perspective on the role of dynamic CKM in emerging 6G systems.

Problem

• dynamic channel
• channel knowledge map
• time-varying channel
• 6G networks
• channel acquisition

Innovation

• Dynamic Channel Knowledge Map
• Time-varying channel modeling
• 6G wireless networks
• Physical-layer signal processing
• Integrated sensing and communications