Link Both Views: Cross-Image Routing Attention for Dual-Endpoint V2I Beam-Pair Power Prediction

Shiliang Lu; Lu Bai; Ziwei Huang; Xiang Cheng

eess.SPOct 13, 2026

Link Both Views: Cross-Image Routing Attention for Dual-Endpoint V2I Beam-Pair Power Prediction

Authors: Shiliang Lu, Lu Bai, Ziwei Huang, Xiang Cheng

Organizations: Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China · School of Software, Shandong University · State Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing 100871, China

Abstract

For sensing-aided communications, single-endpoint sensing offers only a partial view of propagation, motivating link-centric sensing across both endpoints. We propose cross-image routing attention (CIRA) for sparse association and mutual enhancement of propagation-relevant features from dual-endpoint RGB images. Building on CIRA, CiFormer predicts millimeter-wave (mmWave) beam-pair power matrices for vehicle-to-infrastructure (V2I) communications. Experiments show that CiFormer reduces mean absolute error and improves top-1 beam-pair selection accuracy by 3.15 percentage points over the strongest baseline. Visualizations and ablation studies support dual-endpoint sensing and selective cross-view interaction in capturing propagation-relevant dependencies, suggesting broader applications in wireless tasks beyond beam prediction.

Figures & tables

Fig. 1: Illustration of the proposed cross-image routing attention. (a) Physical motivation of Tx–Rx co-visible reflection in wireless channels, where objects simultaneously visible from both views are more likely to contain scatterers that give rise to NLoS paths. (b) Cross-image routing attention, which enhances the modeling of potential reflection-related windows guided by the Tx–Rx co-visible propagation mechanism.

Fig. 2: Overall architecture of CiFormer, with detailed structures of the CiFormer block on the left and the BPPM prediction head on the right.

Fig. 3: Bird’s-eye view of the V2I simulation scenario, with endpoint locations, vehicle trajectories, BS orientations, and seven vehicle (Tx)–BS (Rx) links.

Model	Params. (M)	RMSE (dB)	MAE (dB)	Acc@1 (%)	Acc@3 (%)	Mean BF loss (dB)
ViT	10.995	6.318	4.143	68.25	91.81	0.943
MobileNetV4	14.950	6.553	4.156	68.39	91.16	0.924
MambaOut	11.831	6.014	3.864	69.26	93.03	0.658
CiFormer	10.986	5.810	3.638	72.41	94.68	0.641

TABLE I: Comparison of BPPM Prediction Performance

Fig. 4: Grad-CAM visualizations of CiFormer, ViT, MobileNetV4, and MambaOut. CiFormer’s activation maps emphasize the building and roadside trees in both views, whereas those of the baselines exhibit weaker cross-view correspondence.

Variant	RMSE (dB)	MAE (dB)	Acc@1 (%)	Acc@3 (%)	Mean BF loss (dB)
w/o Tx	10.097	7.047	40.95	65.30	6.590
w/o Rx	12.344	6.867	54.96	73.56	4.414
w/o Interaction	6.001	3.740	70.98	93.89	0.723
w/o top- $k$ routing	5.922	3.672	71.19	93.97	0.703
w/o Gating	5.827	3.678	71.19	93.53	0.756
w/o SAB	5.893	3.767	71.05	93.53	0.708

TABLE II: Ablation Study of Dual-Endpoint Inputs and CIRA Components

Cross-View Attention

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Robust Beam Prediction for V2X Networks with Multi-Modal Sensing

Chen Shang, Dinh Thai Hoang, Diep N. Nguyen, Jiadong Yu

School of Electrical and Data Engineering, University of Technology Sydney, Australia · Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

Integrated Sensing and Communication V2X Communication Multimodal Sensor Fusion

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Integrated sensing and communication (ISAC) provides a promising foundation for beam prediction in future vehicle-to-everything (V2X) networks. However, existing sensing-assisted beamforming methods still rely heavily on radio-frequency sensing, which may become unreliable in complex vehicular environments. Meanwhile, the growing availability of heterogeneous sensors, such as cameras and LiDAR, offers new opportunities to improve beam prediction through richer environmental perception. Motivated by this, this paper proposes a multi-modal beam prediction framework for V2X networks. Specifically, we develop BeamTransFuser, a hierarchical Transformer-based architecture that progressively fuses camera, LiDAR, radar, and GPS observations for robust beam prediction. In addition, to handle possible missing modalities in practical deployment, we introduce a generative module that reconstructs missing modality features from the available observations. Experimental results on a real-world multi-modal V2X dataset show that the proposed framework consistently outperforms representative baselines, while the generative module further improves robustness under incomplete sensing conditions.

Abstract

Cross-View Attention

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