Wireless emulation is an essential technique for efficiently designing and verifying wireless systems in a virtual space. To accurately emulate the behavior of wireless systems in various scenarios, it is crucial to simulate radio propagation with high accuracy according to the environment. Several recent studies have proposed site-specific modeling methods using supervised machine learning. However, supervised learning generally requires a large amount of training data, and acquiring a large amount of measurement data on the propagation characteristics requires significant resources. Therefore, the model accuracy with few measurement data is an issue. This paper quantitatively clarifies the relationship between the amount of training data and the model accuracy. Furthermore, we propose a propagation modeling method with high accuracy with few data using fine-tuning. In this method, we pre-train the propagation model at a frequency with many measurement data available. Using pre-trained weights as initial values enables highly accurate propagation modeling with few data at a new frequency. Evaluation using measurement data in the 800 MHz, 2.1 GHz, and 3.5 GHz bands acquired in urban areas demonstrates the effectiveness of the proposed method in terms of accuracy and training time.


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    Title :

    Fine-Tuning for Propagation Modeling of Different Frequencies with Few Data


    Contributors:


    Publication date :

    2022-09-01


    Size :

    20304829 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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