Learning-Based Path Loss Estimation Using Multiple Spatial Data and System Parameters
- Resource Type
- Conference
- Authors
- Inoue, Kazuya; Imaizumi, Keita; Ichige, Koichi; Nagao, Tatsuya; Hayashi, Takahiro
- Source
- 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Vehicular Technology Conference (VTC2022-Fall), 2022 IEEE 96th. :1-5 Sep, 2022
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Signal Processing and Analysis
Transportation
Wireless communication
Deep learning
Vehicular and wireless technologies
Propagation
Estimation
Propagation losses
Spatial databases
- Language
- ISSN
- 2577-2465
We propose a novel path loss estimation method based on deep learning with some newly defined system parameters and images. Estimating the radio wave propagation environment is one of the key techniques for indoor/outdoor high-speed wireless communication. The radio wave propagation environment is basically a multipath environment, and path loss characteristics should be estimated under various environments. The authors have already proposed path loss estimation methods based on machine learning and spatial image data. The purpose of this paper is to further enhance the path loss estimation accuracy by appropriately selecting the input parameters and the CNN/FNN model structure.