Year: 2025
Type: article
Abstract: As e-Um 5G (private 5G) has emerged as a key infrastructure for digital transformation, accurate high-resolution radiowave mapping has become crucial for stable service and efficient deployment. However, traditional kriging has a limited ability to model the complex nonlinearities of radio environments. To enhance e-Um 5G map accuracy, this study proposes a novel hybrid interpolation method that combines deep learning and residual kriging. Using actual data, the proposed method was found to be superior to traditional kriging in terms of the prediction accuracy and map resolution, particularly with varying sample sizes.
Authors: Min-Joon Kim, Beom-Joo Kim, Lee Won-Cheol
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Award: G4087473340
OpenAlex ID: W7124902693