Multi-modal datasets (currently 7 in total)
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Dataset for "SoM-Aided FDD Precoding with Sensing Heterogeneity: A Vertical Federated Learning Approach"
📌 Overview
This dataset is constructed based on the triangular block scenario in the M3SC dataset, including CSI data between the roadside unit and seven passing vehicles, downlink received signal data of vehicles, 64-line LiDAR point cloud data collected by vehicles, RGB image data collected by vehicles, and GPS positioning data of vehicles. The above-mentioned dataset consists of a total of 3000 samples. The frequency of the communication system's downlink channel is set to 4.95GHz, and the frequency of the uplink channel is set to 4.85GHz. The laser radar has a field of view angle of 40°. The roadside unit is equipped with a 16*8 UPA array, and the vehicle is equipped with a single antenna. In addition, GPS signals are set to disappear in a certain area of the scenario to fit real-world applications.
📜 Related paper
H. Zhang, S. Gao, W. Wen, X. Cheng and L. Yang, "Synesthesia of Machines (SoM)-Aided Online FDD Precoding via Heterogeneous Multi-Modal Sensing: A Vertical Federated Learning Approach," IEEE Transactions on Mobile Computing, early access, 2026.
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