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

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 the target channel information in the CSI-tuple can be estimated directly by its corresponding LAV positions, together with communication measurements and geographic environment maps. Then, a high-efficiency multimodal framework is proposed accordingly, which includes a correlation-based multimodal fusion (Corr-MMF) module, a multimodal representation (MMR) module, and a CSI regression (CSI-R) module. Numerical results show that our proposed framework can efficiently construct 3D-CF and achieve at least 27.5% higher accuracy than the state-of-the-art algorithms under different communication scenarios, demonstrating its competitive performance and excellent generalization ability. We also analyze the computational complexity and illustrate its superiority in terms of the inference time.

Figures (9)

• Figure 1: A typical ground-to-LAV communication scenario, where all possible channel components include one LOS path and several reflected paths. Its propagation geometry takes the BS as the origin.
• Figure 2: Diagram of the proposed 3D-CF MultiModal framework. This scheme includes three essential modules: the Corr-MMF module, the MMR module, and the CSI-R module. The first two modules are designed to extract features of CSI distribution and geographic environments in horizontal and vertical directions, respectively, and the third module is responsible for spacial CSI prediction and 3D-CF reconstruction.
• Figure 3: Diagram of the Corr-MMF module and its CAM.
• Figure 4: Diagram of the MMR module and its SAM.
• Figure 5: Diagram of the proposed CSI-R module. The network includes a feature embedding layer and a fully connected regression layer.