Path Loss Estimation at Sub-6 GHz and Millimeter Wave Frequencies Using Fine-Tuning

Abstract

Accurate estimation of path loss poses a challenge given its strong reliance on complex propagation environments that may change frequently for example due to new building constructions. Furthermore, the propagation channel at millimeter wave (mmWave) frequencies is highly susceptible to blockage, leading to notable variations in path loss values among different links at comparable distances, which renders path loss estimation based on empirical model less representative. In this paper, we tackle the challenge of accurate path loss estimation for sub-6 GHz and mmWave frequencies when working with a limited dataset of satellite images. In contrast to current literature which relies on satellite images in conjunction with supplementary models, features, or algorithms for path loss estimation, our approach involves directly estimating the path loss from satellite images. Specifically, we employ convolutional neural networks (CNNs), based on VGG-16 and ResNet-50 architectures, to fine-tune existing pretrained models, aiming to achieve accurate path loss estimation. By incorporating the appropriate layers to be fine-tuned, we are able to accelerate the training process. Furthermore, the proposed CNN path loss models yields lower prediction errors compared to the 3GPP 38.901 urban macro (UMa) empirical model by up to 5.5 dB in root mean square error (RMSE) and 4.6 dB in mean absolute error (MAE). In many instances involving different fine-tuning variants and frequencies, VGG-16 achieves lower prediction errors compared to ResNet-50. If processing times and resource consumption are a priority, ResNet-50 can be a viable choice, especially if tolerating increases in RMSE and MAE by up to 0.9 dB and 1 dB, respectively, is acceptable.

Keywords

• Convolutional neural network (CNN)
• empirical channel model
• image processing
• millimeter wave
• network planning
• path loss