Site Prediction Model for the over Rooftop Path in a Suburban Environment at Millimeter Wave

Abstract

This paper proposes the site-specific pathloss model for a small town in a suburban environment at millimeter wave. Also, the site general characteristics are provided in the entire measurement area of the small town. The proposed pathloss model is based on the moving measurement campaign according to the distance between a transmitter and a receiver in the candidate millimeter wave band or future five-generation service. In addition, the probability characteristics of pathloss such as the cumulative density function are shown to estimate the spreading characteristics of the wideband signal with a five-hundred-MHz bandwidth. Measurement was performed for the small town in a suburban covered with low-rise commercial restaurants or houses. Transmitting signals propagate through the over rooftop of low-rise houses of average ten meters height. Their multipath signals may be arrived at a receiving station located at the below roof of the house by reflection, diffraction, and scattered mechanism. A current propagation model for the over rooftop is based on generic or geometrical optic equations in a fully non-line-of-sight environment. But, non-line-of-sight cases in a realistic suburban environment include partly the open space between houses. Therefore, the definition of the propagation model of this sight-specific case is very important so clearly as to show the difference for the over rooftop according to the different displacements of houses in a non-line-of-sight environment. Finally, we suggest the improved propagation model of pathloss that can reflect various environments in a small town.

Empirical Model Including the Statistics of Location Variability for the Over-Rooftop Path in the 32 GHz Band

“…We also calculated the site-specific basic transmission loss (ITU-R) of the over-rooftop path for suburban areas provided by the Recommendation ITU-R P.1411 [7], showing the results through the green dash-dotted line in Figure 11 (Calculation conditions: station 1 antenna height, h 1 = 44 m, station 2 antenna height, h 2 = 1.5 m, average height of buildings, h r = 10 m, street width, w = 15 m, street orientation with respect to the direct path, ϕ = 90 • ). The difference between the median curve for measurement basis and the ITU-R prediction curve was due to the propagation environment of the electromagnetic wave [17], [20], [21]. The ITU-R curve appears to show the approximate results of the site-specific prediction model where the buildings were the same height and there were no gaps between buildings, while the median curve based on the measurement data shows the analyzed results in a real environment, as shown in Figure 2, where the characteristics of the roofs of buildings varied in from flat to sloped and there were gaps between buildings.…”

Performance study of path loss models at 14, 18, and 22 GHz in an indoor corridor environment for wireless communications

“…The mmWave regime provides a bandwidth higher than 400 over commercial applications, which is extremely large compared to today's 4 long-term-evolution (LTE) 20 cellular channels [16], [22]. Assuredly, the mmWave frequency spectrum plays an essential role in 5 wireless communications [1], [14].…”

“…Because mmWave signals have a severely limited range, hotspot cells will need to become the focus of future mobile applications in both urban and suburban environments. The small cell radius will be between 100 and 500 meters in outdoor urban areas at the mmWave frequency bands [16]. Consequently, the cell coupling range and the path loss values will change quickly.…”

Millimeter Wave Path Loss Modeling for 5G Communications Using Deep Learning With Dilated Convolution and Attention

“…Therefore, path loss modeling for mmWave propagation plays a vital role in designing and analyzing 5G communication systems. Three types of conventional path loss modeling methods have been investigated in previous studies, namely empirical methods [3]- [7], deterministic methods [8], [9], and machine learning-based (ML-based) methods [10]- [18].…”