The realization of Cyber-Physical Systems (CPS) signals the potential resolution of various societal challenges. The exchange of diverse information between the physical and virtual realms necessitates the use of wireless systems, including those beyond 5G. In particular, the application of wireless emulators for the verification of wireless systems in virtual spaces is anticipated, enabling the efficient validation of systems with numerous wireless devices by replicating real-world communication environments in virtual spaces. However, accurate wireless communication emulation requires site-specific and high-precision radio wave propagation models. Recently, machine learning-based approaches have been proposed for modeling site-specific radio wave propagation characteristics. However, conventional models typically use training and test data from the same region, which can lead to degraded estimation accuracy when applied to unknown regions. This paper proposes a high-generalization model that improves estimation accuracy in unknown regions by using propagation path information as input features, based on the physical propagation mechanisms. The proposed model estimates a propagation path using machine learning and further uses this estimated path as input for machine learning to estimate propagation loss. When evaluating the estimation accuracy of propagation loss in unknown regions different from the training region, the proposed model reduced the RMSE from 5.52 dB to 4.69 dB compared to the conventional model, achieving an estimated error improvement of approximately 15%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Propagation Path-Informed High-Generalization Path Loss Model for Unknown Region Estimation


    Contributors:


    Publication date :

    2024-06-24


    Size :

    692580 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Evaluation of High-Performance Radio Propagation Simulation Method in Path Loss Estimation

    Tomie, Takahiro / Suyama, Satoshi / Kitao, Koshiro et al. | IEEE | 2023


    Dense Crowd Flow-Informed Path Planning

    Pruc, Emily / Zilberstein, Shlomo / Biswas, Joydeep | ArXiv | 2022

    Free access


    WEATHER-INFORMED PATH PLANNING FOR A VEHICLE

    CUI MICHAEL / KWON HYUKSEONG / ROMERO RODOLFO VALIENTE et al. | European Patent Office | 2025

    Free access

    PATH ESTIMATION DEVICE AND PATH ESTIMATION METHOD

    TAKABAYASHI YUKI / OBATA YASUSHI | European Patent Office | 2021

    Free access