Construction of 6G Omni-Scenario Knowledge Base for On-Demand Service Using Channel Knowledge Map

Zifan Sha, Changle Li, Wenwei Yue, Xiaojian Yang, Yuchuan Fu, Luqiao Wang, Nan Cheng, Mahdi Boloursaz Mashhadi, Zhili Sun

Keywords: 6G, omni-scenario, on-demand service, channel knowledge map, knowledge base construction

Abstract: With the rapid evolution of 6G networks, the need for on-demand service across diverse scenarios continues to grow. However, a key challenge lies in the lack of a structured knowledge infrastructure that can possess the capability for standardized representation and accurately reflect the comprehensive 6G scenarios. To address this, we propose OSOD 6G, a 6G omni-scenario knowledge base empowered by Channel Knowledge Map (CKM), designed to capture and infer the essential features of wireless channels under varying environments. Firstly, by elucidating the intrinsic relationships among subject, environment, requirement, and knowledge through an ontological framework, OSOD 6G covers seven representative scenarios including multi-stage urban areas, plain, mountain, and ocean. Secondly, we deploy heterogeneous transmitters such as omnidirectional base stations, directional base stations, and satellites across distinct urban development stages, and densely position receivers both in aerial and terrestrial domains. Then, CKM inference is used to extract channel data, which is then filtered, structured, and indexed under a unified schema. The resulting dataset includes 759,590 structured entries, enabling efficient scenario-aware inference. Finally, simulation results reveal significant channel characteristic variations across different deployment configurations and environmental types, underscoring the necessity for service customization. Moreover, in handling large-scale CKM data, the XGBoost model demonstrates superior fitting accuracy compared with the baseline, achieving an R-squared of 94.94% and a root mean square error (RMSE) of 0.8394.

Submission Number: 10