For beyond the fifth-generation (5G) and future wireless communications, spatial consistency that represents the correlation between propagation channel characteristics in close proximity has become one of the major issues in channel modeling to describe channels more realistically in emerging scenarios, such as device-to-device (D2D). In this study, we propose a novel path loss model based on multi-dimensional Gaussian process regression (GPR) that provides spatial consistency to channels in propagation environment by predicting local shadow fading while fitting large-scale path loss from measured data. The proposed model has a special structure consisting of a radial mean function and local shadow fading term, which are modeled by independent Gaussian Processes. In contrast to the log-distance path loss model and other regression-based models, the proposed model trains two functions simultaneously; thus, predicts path loss well by capturing both global tendency and local correlation. Moreover, because the proposed model is based on GPR, it provides uncertainty of the predicted path loss. We validated the performance of the proposed model in terms of prediction accuracy with the measurement datasets from two different indoor environments. Our experiments showed that the proposed model predicts better than the log-distance path loss model, especially when spatial correlation becomes significant. The proposed model can be used to simulate path loss in a general environment after training the measurement data.

Excess loss due to building shadowing is regarded as an obstacle effect by buildings existing on the radio propagation path in relation to the service coverage performance of the next-generation mobile communication. In order to overcome the limitations of a geometric or deterministic modeling of existing obstacle loss models (ITU-R, W-I, and W-B model) and increase the accuracy of prediction of excess loss below 10 GHz in real urban environments, the improved empirical model is proposed by adding new variable and optimized fitting parameter values to the existing ITU-R model's formula. New variable and optimized parameter values reflect complex building structure and building height and their propagation impact through measurement. For validation of the proposed model, the self-verification and cross-verification were performed using the measured data from Korea and Japan. The measured data as well as the proposed model were presented as root-mean-square error that can be compared with other existing models in accuracy. The new proposed empirical model showed good agreement with the measured data compared to the existing geometric and deterministic models. In conclusion, the proposed model can be applied to the slant path link below 10 GHz frequency.

Measuring the diffraction loss for high frequencies, long distances, and large diffraction angles is difficult because of the high path loss. Securing a well‐controlled environment to avoid reflected...

Rainfall is considered as one of the most crucial atmospheric elements that cause attenuation in signal propagation, especially in high-frequency range for fifth-generation (5G) and beyond wireless networks. To unveil the complex relationships between rain attenuation and other factors including rainfall rate, we propose a new rain attenuation prediction model for terrestrial line-of-sight (LoS) propagation using Gaussian Process Regression (GPR). In the proposed model the Recommendation ITU-R P.530 (called the ITU-R model) is used as the mean function in GPR, and to capture the deviation of measured rain attenuation from the ITU-R model we develop a latent function in GPR motivated from the ITU-R model. We validate with the ITU-R study group 3 databank (DBSG3) that the proposed model provides high prediction accuracy.

Atmospheric elements affect signal propagation and cause significant signal loss called atmospheric attenuation, especially in high-frequency range including sub-THz frequencies. Atmospheric attenuation mainly consists of gaseous, fog, and rain attenuation and the traditional approach considers them separately to develop good prediction models. However, the traditional models are not accurate and even unavailable for the high-frequency range such as sub-THz frequencies. In this letter, we propose a new attenuation model to predict atmospheric attenuation for terrestrial line-of-sight (LoS) propagation at high frequencies like sub-THz. The proposed model is based on Gaussian Process Regression (GPR). To train our model with a big measurement dataset, we use a scalable variant of GPR called Blockbox Matrix-Matrix Gaussian Process (BBMM-GP). We validate our model with a dataset obtained from a long-term measurement campaign at sub-THz frequencies. Our experiments show that our model significantly outperforms the existing model. We also show that our model provides reliable prediction intervals of atmospheric attenuation.

Rain attenuation based on the drop size distribution (DSD) with different rainfall rates (R) at low THz frequencies is investigated in this paper. The rain attenuation is calculated using the DSD measured for one year and the extinction cross-section (ECS) by the Mie scattering theory. Moreover, the obtained specific rain attenuation is verified by the empirical model using the measurement system consisting of a transmitter, a receiver, and weather measurement units. We measured the received power against the uniform transmitted power at 240, 270, and 300 GHz on the rooftop of the National Radio Research Agency (RRA) in Korea during the same period as the DSD measurement period. After curve fitting by regression analysis, we compared both rain attenuations obtained in two methods with the recommendation International Telecommunication Union Radiocommunication Sector (ITU-R) P.838-3. The root mean square errors (RMSEs) of the DSD model are 2.8977, 2.8646, and 2.8331 at 240, 270, and 300 GHz, respectively. The calculated result using the Mie scattering and the measured DSD methods shows the best fit to the data of the ITU-R recommendation for a rainfall rate of up to 5 mm/h. On the other hand, the empirical results using the T/Rx antenna system are slightly higher compared to the data of the ITU-R recommendation. As the rainfall rate increases, the difference between our results and ITU-R recommendation increases. This study will be useful for predicting rain attenuation for terrestrial wireless links operating at low THz frequencies.