{"ID":2839326,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.15030","arxiv_id":"2511.15030","title":"WiCo-PG: Wireless Channel Foundation Model for Pathloss Map Generation via Synesthesia of Machines","abstract":"A wireless channel foundation model for pathloss map generation (WiCo-PG) via Synesthesia of Machines (SoM) is developed for the first time. Considering sixth-generation (6G) uncrewed aerial vehicle (UAV)-to-ground (U2G) scenarios, a new multi-modal sensing-communication dataset is constructed for WiCo-PG pre-training, including multiple U2G scenarios, diverse flight altitudes, and diverse frequency bands. Based on the constructed dataset, the proposed WiCo-PG enables cross-modal pathloss map generation by leveraging RGB images from different scenarios and flight altitudes. In WiCo-PG, a novel network architecture designed for cross-modal pathloss map generation based on dual vector quantized generative adversarial networks (VQGANs) and Transformer is proposed. Furthermore, a novel frequency-guided shared-routed mixture of experts (S-R MoE) architecture is designed for cross-modal pathloss map generation. Simulation results demonstrate that the proposed WiCo-PG achieves improved pathloss map generation accuracy through pre-training with a normalized mean squared error (NMSE) of 0.012, outperforming the large language model (LLM)-based scheme, i.e., LLM4PG, and the conventional deep learning-based scheme by more than 6.98 dB. The enhanced generality of the proposed WiCo-PG can further outperform the LLM4PG by at least 1.37 dB using 2.7% samples in few-shot generalization.","short_abstract":"A wireless channel foundation model for pathloss map generation (WiCo-PG) via Synesthesia of Machines (SoM) is developed for the first time. Considering sixth-generation (6G) uncrewed aerial vehicle (UAV)-to-ground (U2G) scenarios, a new multi-modal sensing-communication dataset is constructed for WiCo-PG pre-training,...","url_abs":"https://arxiv.org/abs/2511.15030","url_pdf":"https://arxiv.org/pdf/2511.15030v1","authors":"[\"Mingran Sun\",\"Lu Bai\",\"Ziwei Huang\",\"Xuesong Cai\",\"Xiang Cheng\",\"Jianjun Wu\"]","published":"2025-11-19T01:57:00Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Mixture of Experts\",\"Transformer\",\"Large Language Model\",\"Language Model\",\"Generative Adversarial Network\"]","has_code":false}