A Data Augmentation Approach to 28GHz Path Loss Modeling Using CNNs
- Resource Type
- Conference
- Authors
- Kwon, Bokyung; Kim, Youngbin; Lee, Hyukjoon
- Source
- 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Artificial Intelligence in Information and Communication (ICAIIC), 2023 International Conference on. :825-829 Feb, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Deep learning
Base stations
Convolution
Communication systems
Neural networks
Millimeter wave technology
Training data
path loss modeling
data augmentation
CNN
5G
- Language
- ISSN
- 2831-6983
Millimeter waves are easily influenced by the surrounding environment, making it difficult to predict path loss values for 28GHz communication systems. Recently, deep learning approaches have become popular mainly thanks to their superior performance in terms of prediction accuracy, generalizability as well as local adaptability. These deep learning approaches require a sufficient number of training data which often lacks variability with respect to the parameter values of base station configuration if not unavailable at all. This paper proposes to use the data augmentation approach to address these two issues by using a simulator to generate predicted data for the arbitrary values of base station parameters. It is shown that a Convolution Neural Network (CNN) trained with both measurement and augmented data outperforms a vanilla CNN model trained with measurement data only and that it can make accurate predictions for arbitrary base station configurations.