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

Chenjie Xie; Li You; Ruirong Chen; Gaoning He; Xiqi Gao

cs.ITMar 26, 2026

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

Authors: Chenjie Xie, Li You, Ruirong Chen, Gaoning He, Xiqi Gao

Organizations: National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China · Purple Mountain Laboratories, Nanjing 211111, China · Huawei Technologies Co., Ltd., Shenzhen 518129, China

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 & tables

Fig. 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.

Fig. 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.

Panel of Figure 3 — Fig. 3: Diagram of the Corr-MMF module and its CAM.

Panel of Figure 4 — Fig. 4: Diagram of the MMR module and its SAM.

Fig. 5: Diagram of the proposed CSI-R module. The network includes a feature embedding layer and a fully connected regression layer.

Parameters	Value
Communication scenario	urban macro-cell / micro-cell
Size of the target region	$256\times 256$
Number of antenna elements for BS	$64$
BS height	$25$ m
LAV flight altitudes	$25-80$ m
Sampling height of $\mathcal{G}_{\rm gro}$	$1.5$ m

TABLE I: Parameters used to generate datasets in the platform of Sionna.

Parameter	Module
Parameter	Corr-MMF	MMR	CSI-R
Epochs	60	50	15
Delay Epochs	35	35	10
Learning Rate	0.005	0.001	0.0001
Batch Size	128	128	32
Optimizer	Adam

TABLE II: Hyper-parameters of Corr-MMF, MMR, and CSI-R modules in 3D-CF MultiModal framework.

Fig. 6: MAE and RMSE performance of the 3D-CF multimodal framework under different $\lambda$ .

Fig. 7: The convergence behavior of the validation loss for the Corr-MMF module under different $\lambda$ .

Panel of Figure 10 — Fig. 8: Illustrations of 3D-CF under four randomly selected communication scenarios. The RSS distribution at four horizontal planes, specifically at heights of 10 m, 20 m, 30 m, and 40 m, are illustrated along the Z axis.

	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE
	Scenario 1		Scenario 2		Scenario 3		Scenario 4
Kriging [ 16 ]	0.522	0.467	0.348	0.287	0.340	0.289	0.459	0.384
GPR [ 17 ]	0.343	0.223	0.291	0.201	0.304	0.161	0.325	0.194
GAN [ 30 ]	0.234	0.195	0.208	0.156	0.269	0.221	0.266	0.223
Federated Learning [ 72 ]	0.088	0.067	0.041	0.034	0.035	0.029	0.056	0.039
Multimodal framework (ours)	0.069 $\downarrow$	0.044 $\downarrow$	0.026 $\downarrow$	0.019 $\downarrow$	0.024 $\downarrow$	0.021 $\downarrow$	0.045 $\downarrow$	0.027 $\downarrow$

TABLE III: Comparison of the 3D-CF multimodal framework with state-of-the-art approaches under different communication scenarios regarding the RMSE and MAE performance.

Geographic environments $\mathcal{E}$		Measurements $\mathcal{G}_{\rm gro}$	Evaluation metrics
$\mathcal{E}_{h}$	$\mathcal{E}_{v}$	Measurements $\mathcal{G}_{\rm gro}$	RMSE	MAE
✓	✓	✓	0.045	0.027
✓	✓	✗	0.047	0.028
✗	✓	✓	0.047	0.029
✓	✗	✓	0.687	0.675
✗	✗	✓	0.700	0.692

TABLE IV: Contribution of each input modality to the 3D-CF multimodal framework.

Corr-MMF module		MMR module	Evaluation metrics
CAM	TAM	SAM	RMSE	MAE
✓	✓	✓	0.045	0.027
✗	✓	✓	0.065	0.052
✓	✗	✓	0.546	0.455
✓	✓	✗	0.290	0.282

TABLE V: Ablation study on different attention mechanisms for 3D-CF construction.

Fig. 9: Comparison of the 3D-CF inference time between the proposed multimodal framework and state-of-the-art approaches.

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Wireless communications studies how information is sent over radio links and how networks manage signals, spectrum, and resources reliably.

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Apr 30, 2026J/K move · Enter open · S save

Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework

Zhenzhou Jin, Li You, Xiang-Gen Xia, Xiqi Gao

National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China · Purple Mountain Laboratories, Nanjing 211100, China · Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA

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Channel fingerprint (CF) is considered a key enabler for facilitating the acquisition of channel state information (CSI) in massive multiple-input multiple-output (MIMO) communication systems. In this work, we investigate a novel type of CF that stores statistical CSI (sCSI) at each potential location, referred to as statistical CF (sCF). Specifically, we reveal the relationship between sCSI, namely the channel spatial covariance matrix (CSCM), and the channel power angular spectrum (CPAS). Building on this foundation, we construct a unified tensor representation of the sCF and further reduce its dimension by exploiting the eigenvalue decomposition of the CSCM and its correlation with the PAS. Considering the practical constraints imposed by measurement cost, privacy, and security, we focus on three representative scenarios and uniformly formulate them as tensor restoration tasks. To this end, we propose a unified tensor-based learning architecture, termed LPWTNet. The architecture incorporates a closed-form Laplacian pyramid (LP) decomposition and reconstruction framework that replaces the traditional encoder-decoder structure, enabling efficient inference while capturing multi-scale frequency subband characteristics of the sCF. Additionally, a shared mask learning strategy is introduced to adaptively refine high-frequency sCF components through level-wise adjustments. To achieve a larger receptive field without over-parameterization, we further propose a small-kernel convolution mechanism based on the wavelet transform (WT), which decouples convolution across different frequency components of the sCF and enhances feature extraction efficiency. Extensive experiments show that the proposed approach delivers competitive reconstruction accuracy and computational efficiency across various sCF construction scenarios when compared with state-of-the-art baselines.

Abstract

Wireless Communications

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