In high-speed wireless communications, an analysis of the propagation characteristics is an important process. Information on the propagation characteristics suitable for each environment significantly helps in the design of mobile communications. This paper presents the analysis results of radio propagation characteristics in outdoor environments for a new mobile wireless system at 781 MHz. To avoid the interference of Korean DTV broadcasting, we measure the channel characteristics in urban, suburban, and rural areas on Jeju Island, Republic of Korea, using a channel sounder and 4×4 antenna. The path loss (PL) measurement results differ from those of existing propagation models by more than 10 dB. To analyze the frequency characteristics for Korean propagation environments, we derive various propagation characteristic parameters: PL, delay spread, angular spread, and K-factor. Finally, we verify the validity of the measurement results by comparing them with the actual measurement results and 3D ray-tracing simulation results.

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.

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.

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.

In this paper, statistical inference is used to predict a practical linear equation for determining signal loss rates over distance in a suburban over-rooftop path under both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. Unlike existing prediction equations, the proposed model provides an 80% confidence interval with the same slope as a regression equation and an appropriate intercept based on distance. Additionally, the proposed Gaussian mixture model clustering algorithm can classify unlabeled real-time measurements as either LoS or NLoS with high accuracy, significantly improving the convenience of signal measurement.