TY - GEN
T1 - Meta-Learning-Based Scheme for Cross-Scenario Path Loss Prediction
AU - Wang, Songqiao
AU - Zhao, Shenghui
AU - Zhang, Yan
AU - Zhang, Kaien
AU - Song, Jiupeng
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - As the sixth generation mobile communication system (6G) evolves, its envisioned paradigms have fragmented communication scenarios into specialized domains, requiring tailored physical-layer designs. Existing channel models face cross-scenario generalization deficiencies, while deep learning (DL)-based methods rely on large training datasets and lack adaptability to new scenarios. This paper presents a generalizable cross-scenario channel modeling scheme, including a Meta-learning-based Channel Modeling (MCM) algorithm, tri-modal input, and RadioNeXt network. Simulations and measurements verify that the proposed method outperforms transfer learning (TL)-based approaches, especially in hybrid-density scenarios, with the RadioNeXt network surpassing ResNet50 and Deep Vision Transformer (DeepViT).
AB - As the sixth generation mobile communication system (6G) evolves, its envisioned paradigms have fragmented communication scenarios into specialized domains, requiring tailored physical-layer designs. Existing channel models face cross-scenario generalization deficiencies, while deep learning (DL)-based methods rely on large training datasets and lack adaptability to new scenarios. This paper presents a generalizable cross-scenario channel modeling scheme, including a Meta-learning-based Channel Modeling (MCM) algorithm, tri-modal input, and RadioNeXt network. Simulations and measurements verify that the proposed method outperforms transfer learning (TL)-based approaches, especially in hybrid-density scenarios, with the RadioNeXt network surpassing ResNet50 and Deep Vision Transformer (DeepViT).
KW - 6G
KW - channel model
KW - communication scenarios
KW - ConvNeXt
KW - meta-learning
UR - https://www.scopus.com/pages/publications/105047056065
U2 - 10.1109/ICCCS69761.2026.11613504
DO - 10.1109/ICCCS69761.2026.11613504
M3 - Conference contribution
AN - SCOPUS:105047056065
T3 - 2026 the 11th International Conference on Computer and Communication Systems, ICCCS 2026
SP - 239
EP - 245
BT - 2026 the 11th International Conference on Computer and Communication Systems, ICCCS 2026
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Computer and Communication Systems, ICCCS 2026
Y2 - 17 April 2026 through 20 April 2026
ER -