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

arXiv: https://arxiv.org/abs/2603.25288 · HTML: https://mlsanity.com/paper/2603.25288/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 73 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 29 of 29)

- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [Large Language Model Enhanced Multi-Agent Systems for 6G Communications](https://mlsanity.com/paper/2312.07850/) (2023)
- [A Review on Explainability in Multimodal Deep Neural Nets](https://mlsanity.com/paper/2105.07878/) (2021)
- [Large AI Model Empowered Multimodal Semantic Communications](https://mlsanity.com/paper/2309.01249/) (2023)
- [A Review on Methods and Applications in Multimodal Deep Learning](https://mlsanity.com/paper/2202.09195/) (2022)
- [Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets](https://mlsanity.com/paper/2111.07574/) (2021)
- [RadioDiff-3D: A 3D×3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication](https://mlsanity.com/paper/2507.12166/) (2025)
- [Environment-Aware Hybrid Beamforming by Leveraging Channel Knowledge Map](https://mlsanity.com/paper/2206.08707/) (2022)
- [Generative AI on SpectrumNet: An Open Benchmark of Multiband 3D Radio Maps](https://mlsanity.com/paper/2408.15252/) (2024)
- [An I2I Inpainting Approach for Efficient Channel Knowledge Map Construction](https://mlsanity.com/paper/2406.09822/) (2024)
- [Sparse Bayesian Learning-Based Hierarchical Construction for 3D Radio Environment Maps Incorporating Channel Shadowing](https://mlsanity.com/paper/2403.08323/) (2024)
- [UAV-aided Radio Map Construction Exploiting Environment Semantics](https://mlsanity.com/paper/2107.10574/) (2021)
- [Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming](https://mlsanity.com/paper/2209.02258/) (2022)
- [Integrated Interpolation and Block-term Tensor Decomposition for Spectrum Map Construction](https://mlsanity.com/paper/2402.17138/) (2024)
- [Deep Multimodal Learning: Merging Sensory Data for Massive MIMO Channel Prediction](https://mlsanity.com/paper/2007.09366/) (2020)
- [Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2412.04074/) (2024)
- [IMNet: Interference-Aware Channel Knowledge Map Construction and Localization](https://mlsanity.com/paper/2412.01279/) (2024)
- [Energy-modified Leverage Sampling for Radio Map Construction via Matrix Completion](https://mlsanity.com/paper/2404.08286/) (2024)
- [Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information](https://mlsanity.com/paper/2505.06900/) (2025)
- [3D Spectrum Mapping Based on ROI-Driven UAV Deployment](https://mlsanity.com/paper/2008.02486/) (2020)
- [Fluid Antenna-Assisted MIMO Transmission Exploiting Statistical CSI](https://mlsanity.com/paper/2312.07895/) (2023)
- [Channel Charting-assisted Non-orthogonal Pilot Allocation for Uplink XL-MIMO Transmission](https://mlsanity.com/paper/2412.20920/) (2024)
- [Vision-Aided Frame-Capture-Based CSI Recomposition for WiFi Sensing: A Multimodal Approach](https://mlsanity.com/paper/2206.01414/) (2022)
- [3D Spectrum Mapping and Reconstruction under Multi-Radiation Source Scenarios](https://mlsanity.com/paper/2403.08513/) (2024)
- [On the Spectral Efficiency of Multi-user Holographic MIMO Uplink Transmission](https://mlsanity.com/paper/2407.10848/) (2024)
- [CF-CGN: Channel Fingerprints Extrapolation for Multi-band Massive MIMO Transmission based on Cycle-Consistent Generative Networks](https://mlsanity.com/paper/2412.20885/) (2024)
- [Massive MIMO-OFDM Channel Acquisition with Time-Frequency Phase-Shifted Pilots](https://mlsanity.com/paper/2505.04933/) (2025)

## Abstract (arXiv)

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.