5G 3GPP-like Channel Models for Outdoor Urban Microcellular and Macrocellular Environments

For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.Comment: To be published in 2016 IEEE 83rd Vehicular Technology Conference Spring (VTC 2016-Spring), Nanjing, China, May 201

Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications

This paper compares three candidate large-scale propagation path loss models for use over the entire microwave and millimeter-wave (mmWave) radio spectrum: the alpha-beta-gamma (ABG) model, the close-in (CI) free space reference distance model, and the CI model with a frequency-weighted path loss exponent (CIF). Each of these models have been recently studied for use in standards bodies such as 3GPP, and for use in the design of fifth generation (5G) wireless systems in urban macrocell, urban microcell, and indoor office and shopping mall scenarios. Here we compare the accuracy and sensitivity of these models using measured data from 30 propagation measurement datasets from 2 GHz to 73 GHz over distances ranging from 4 m to 1238 m. A series of sensitivity analyses of the three models show that the physically-based two-parameter CI model and three-parameter CIF model offer computational simplicity, have very similar goodness of fit (i.e., the shadow fading standard deviation), exhibit more stable model parameter behavior across frequencies and distances, and yield smaller prediction error in sensitivity testing across distances and frequencies, when compared to the four-parameter ABG model. Results show the CI model with a 1 m close-in reference distance is suitable for outdoor environments, while the CIF model is more appropriate for indoor modeling. The CI and CIF models are easily implemented in existing 3GPP models by making a very subtle modification -- by replacing a floating non-physically based constant with a frequency-dependent constant that represents free space path loss in the first meter of propagation.Comment: Open access available at: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=743465

Measurement-based Close-in Path Loss Modeling with Diffraction for Rural Long-distance Communications

In this letter, we investigate rural large-scale path loss models based on the measurements in a central area of South Korea (rural area) in spring. In particular, we develop new close-in (CI) path loss models incorporating a diffraction component. The transmitter used in the measurement system is located on a hill and utilizes omnidirectional antennas operating at 1400 and 2250 MHz frequencies. The receiver is also equipped with omnidirectional antennas and measures at positions totaling 3,858 (1,262 positions for LOS and 2,596 positions for NLOS) and 4,957 (1,427 positions for LOS and 3,530 positions for NLOS) for 1400 and 2250 MHz, respectively. This research demonstrates that the newly developed CI path loss models incorporating a diffraction component significantly reduce standard deviations (STD) and are independent of frequency, especially for LOS beyond the first meter of propagation, making them suitable for use with frequencies up to a millimeter-wave.Comment: 5 pages, 5 figure

The probabilistic component of outdoor millimeter wave propagation path loss model considering rain fade

The close-in free space reference distance model CI can be extended to account for the channel shadow fading SF and rain attenuation factors as a different time probability function. Robustness and performance motivated the adoption of combining rain fade and shadowing using the CI model at exceedance probability (0.001%≤P%≤1.0%) and weighing the path losses using a probabilistic distribution of rain fade shadowing as a function of link distance. A probabilistic CI model is proposed considering rain attenuation and shadowing at different probabilities. The mean estimated path loss in this new "hybrid" path loss model is probabilistic. The model can give a close prediction compared to path loss analytically estimated from measured data at 38 GHz at 300m (χσ = 5.22 dB). The difference between path loss predicted from the proposed probabilistic model and path loss analytically estimated from measured path loss at 38 GHz over 300 m at (0.001%≤p%≤1.0%) has been calculated at the tropical region. The findings show a 20 dB per decade loss in signal strength in the equatorial region more than in the temperate areas by considering rain fade for 300 m at 38 GHz. The proposed hybrid probabilistic path loss model can be used as an alternative to conventional propagation path loss models to calculate the directional path loss by increasing the prediction accuracy. The effect of log�normal shadowing, which essentially accounts for the randomness in the shadowing factor around the cell because of the large obstacles, has also been analysed. Additional transmit power is proposed to maintain the fade margin during the rains. Probbilistic path loss models are commonly used in the design and evaluation of millimeter wave wireless systems, which operate at high frequencies and are highly sensitive to the propagation environment. By accounting for the probabilistic nature of the path loss, these models can help to improve the accuracy of predictions and reduce the risk of unexpected performance degradation in real-world deployments