A Scalable Cloud-Edge Collaborative CKM Construction Framework Enabled by a Foundation Prior Model

TL;DR

This paper proposes a scalable cloud-edge framework for constructing channel knowledge maps in 6G networks, leveraging a foundation prior model trained in the cloud to improve accuracy, reduce costs, and enhance deployment scalability.

Contribution

It introduces a novel cloud-edge collaborative approach that decouples CKM prior learning from local observations, enabling scalable, generalizable, and cost-effective CKM construction.

Findings

Achieves competitive accuracy with reduced training costs.

Mitigates negative transfer and improves generalization.

Demonstrates scalability and effectiveness on CKMImageNet dataset.

Abstract

Channel knowledge maps (CKMs) provide a site-specific, location-indexed knowledge base that supports environment-aware communications and sensing in 6G networks. In practical deployments, CKM observations are often noisy and irregular due to coverage-induced sparsity and hardware-induced linear/nonlinear degradations. Conventional end-to-end algorithms couple CKM prior information with task- and device-specific observations, and require labeled data and separate training for each construction configuration, which is expensive and therefore incompatible with scalable edge deployments. Motivated by the trends toward cloud-edge collaboration and the Artificial Intelligence - Radio Access Network (AI-RAN) paradigm, we develop a cloud-edge collaborative framework for scalable CKM construction, which enables knowledge sharing across tasks, devices, and regions by explicitly decoupling a generalizable CKM prior from the information provided by local observations. A foundation model is trained once in the cloud using unlabeled data to learn a generalizable CKM prior. During inference, edge nodes combine the shared prior with local observations. Experiments on the CKMImageNet dataset show that the proposed method achieves competitive construction accuracy while substantially reducing training cost and data requirements, mitigating negative transfer, and offering clear advantages in generalization and deployment scalability.