Istvan Zsolt Kovacs

Propagation Path Loss Models for 5G Urban Micro- and Macro-Cellular Scenarios

This paper presents and compares two candidate large-scale propagation path loss models, the alpha-beta-gamma (ABG) model and the close-in (CI) free space reference distance model, for the design of fifth generation (5G) wireless communication systems in urban micro- and macro-cellular scenarios. Comparisons are made using the data obtained from 20 propagation measurement campaigns or ray- tracing studies from 2 GHz to 73.5 GHz over distances ranging from 5 m to 1429 m. The results show that the one-parameter CI model has a very similar goodness of fit (i.e., the shadow fading standard deviation) in both line-of-sight and non-line-of-sight environments, while offering substantial simplicity and more stable behavior across frequencies and distances, as compared to the three-parameter ABG model. Additionally, the CI model needs only one very subtle and simple modification to the existing 3GPP floating-intercept path loss model (replacing a constant with a close-in free space reference value) in order to provide greater simulation accuracy, more simplicity, better repeatability across experiments, and higher stability across a vast range of frequencies.

A Prediction Study of Path Loss Models from 2-73.5 GHz in an Urban-Macro Environment

It is becoming clear that 5G wireless systems will encompass frequencies from around 500 MHz all the way to around 100 GHz. To adequately assess the performance of 5G systems in these different bands, path loss (PL) models will need to be developed across this wide frequency range. The PL models can roughly be broken into two categories, ones that have some anchor in physics, and ones that curve- match only over the data set without any physical anchor. In this paper we use both real-world measurements from 2 to 28 GHz and ray-tracing studies from 2 to 73.5 GHz, both in an urban-macro environment, to assess the prediction performance of the two PL modeling techniques. In other words, we look at how the two different PL modeling techniques perform when the PL model is applied to a prediction set which is different in distance, frequency, or environment from a measurement set where the parameters of the respective models are determined. We show that a PL model with a physical anchor point can be a better predictor of PL performance in the prediction sets while also providing a parameterization which is more stable over a substantial number of different measurement sets.

A Simple Statistical Signal Loss Model for Deep Underground Garage

In this paper we address the channel modeling aspects for a deep-indoor scenario with extreme coverage conditions in terms of signal losses, namely underground garage areas. We provide an in- depth analysis with regard to the path loss (gain) and large-scale signal shadow fading, and propose a simple propagation model which can be used to predict cellular signal levels in similar deep- indoor scenarios. The measurement results indicate that the signal at 800 MHz band penetrates external concrete walls to reach the lower levels, while for 2000 MHz wall openings are required for the signal to propagate. It is also evident from the study that the shadow fading at different levels of an underground garage are highly correlated. The proposed frequency-independent floor attenuation factor (FAF) is shown to be in range of 5.2 dB per meter deep. Therefore, the attenuation rate in the z dimension is much higher than the in-building attenuation in x and y dimension, which is often assumed at 0.6 dB/m.