Generative AI on SpectrumNet: An Open Benchmark of Multiband 3D Radio Maps
<|reference_start|>Generative AI on SpectrumNet: An Open Benchmark of Multiband 3D Radio Maps: Radio map is an efficient demonstration for visually displaying the wireless signal coverage within a certain region. It has been considered to be increasingly helpful for the future sixth generation (6G) of wireless networks, as wireless nodes are becoming more crowded and complicated. However, the construction of high resolution radio map is very challenging due to the sparse sampling in practical systems. Generative artificial intelligence (AI), which is capable to create synthetic data to fill in gaps in real-world measurements, is an effective technique to construct high precision radio maps. Currently, generative models for radio map construction are trained with two-dimension (2D) single band radio maps in urban scenario, which has poor generalization in diverse terrain scenarios, spectrum bands, and heights. To tackle this problem, we provide a multiband three-dimension (3D) radio map dataset with consideration of terrain and climate information, named SpectrumNet. It is the largest radio map dataset in terms of dimensions and scale, which contains the radio map of 3 spacial dimensions, 5 frequency bands, 11 terrain scenarios, and 3 climate scenarios. We introduce the parameters and settings for the SpectrumNet dataset generation, and evaluate three baseline methods for radio map construction based on the SpectrumNet dataset. Experiments show the necessity of the SpectrumNet dataset for training models with strong generalization in spacial, frequency, and scenario domains. Future works on the SpectrumNet dataset are also discussed, including the dataset expansion and calibration, as well as the extended studies on generative models for radio map construction based on the SpectrumNet dataset.<|reference_end|>
@article{zhang2024generative,
    title={Generative AI on SpectrumNet: An Open Benchmark of Multiband 3D Radio
  Maps},
    author={Shuhang Zhang, Shuai Jiang, Wanjie Lin, Zheng Fang, Kangjun Liu,
  Hongliang Zhang, and Ke Chen},
    journal={arXiv preprint arXiv:2408.15252},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15252},
    primaryClass={eess.SP cs.AI}
}