[2603.25288] CSI-tuples-based 3D Channel Fingerprints Construction Assisted by MultiModal Learning

Computer Science > Information Theory arXiv:2603.25288 (cs) [Submitted on 26 Mar 2026] Title:CSI-tuples-based 3D Channel Fingerprints Construction Assisted by MultiModal Learning Authors:Chenjie Xie, Li You, Ruirong Chen, Gaoning He, Xiqi Gao View a PDF of the paper titled CSI-tuples-based 3D Channel Fingerprints Construction Assisted by MultiModal Learning, by Chenjie Xie and 4 other authors View PDF HTML (experimental) Abstract:Low-altitude communications can promote the integration of aerial and terrestrial wireless resources, expand network coverage, and enhance transmission quality, thereby empowering the development of sixth-generation (6G) mobile communications. As an enabler for low-altitude transmission, 3D channel fingerprints (3D-CF), also referred to as the 3D radio map or 3D channel knowledge map, are expected to enhance the understanding of communication environments and assist in the acquisition of channel state information (CSI), thereby avoiding repeated estimations and reducing computational complexity. In this paper, we propose a modularized multimodal framework to construct 3D-CF. Specifically, we first establish the 3D-CF model as a collection of CSI-tuples based on Rician fading channels, with each tuple comprising the low-altitude vehicle's (LAV) positions and its corresponding statistical CSI. In consideration of the heterogeneous structures of different prior data, we formulate the 3D-CF construction problem as a multimodal regression task, where t...