Fifth-generation (5G) wireless networks are expected to operate at both microwave and millimeter wave (mmWave) frequency bands, including frequencies in the range of 24 to 86 GHz. Radio propagation models are used to help engineers design, deploy, and compare candidate wireless technologies, and have a profound impact on the decisions of almost every aspect of wireless communications. This paper provides a comprehensive overview of the channel models that will likely be used in the design of 5G radio systems. We start with a discussion on the framework of channel models, which consists of classical models of path loss versus distance, large-scale and small-scale fading models, and multiple-input multiple-output channel models. Then, key differences between mmWave and microwave channel models are presented, and two popular mmWave channel models are discussed: the 3rd Generation Partnership Project model, which is adopted by the International Telecommunication Union, and the NYUSIM model, which was developed from several years of field measurements in New York City. Examples on how to apply the channel models are then given for several diverse applications demonstrating the wide impact of the models and their parameter values. These results show that the answers to channel performance metrics such as spectrum efficiency, coverage, hardware/signal processing requirements, etc., are extremely sensitive to the choice of channel models.

Industrial Internet of Things (IIoT) is an emerging area that fifth-generation (5G) mobile communication system penetrates industrial manufacturing applications. The indoor factory has larger space and there are a lot of metal machine tools distributed in it, which makes its radio propagation characteristics and corresponding channel models significantly different from those of the indoor office and indoor hotspot. To support the design and the evaluation of the IIoT techniques, the 3rd Generation Partnership Project (3GPP) released the first 5G IIoT standard model in October 2019. In this article, we give a detailed explanation of this IIoT model and compare it with other indoor models. First, we introduce the standardization of 3GPP IIoT channel model and its motivation. Second, four IIoT subscenarios, which are classified according to the clutter density and antenna height, are described. Third, the potential frequency bands of 5G IIoT are summarized. Fourth, the models of channel parameters, including the path loss and the line-of-sight (LOS) probability, the root mean-square (RMS) delay spread, and the angular spread, are given. Among them, the models of path loss and LOS probability take the antenna height and clutter density into consideration. The model of RMS delay spread changes from frequency-dependent to volume-dependent in order to catch the size variation of factories. Finally, two newly added key channel characteristics, dual mobility and absolute time of arrival, which help to describe the robot movement and positioning, are also presented.

In the 5G-Advanced and beyond systems, multi-frequency cooperative networking will become an inevitable development trend. However, the channels have not been fully investigated at multi-frequency bands and in multi-scenarios by using the same channel sounder, especially for the sub-6 GHz to millimeter-wave (mmWave) bands. In this paper, we carry out channel measurements at four frequency bands (3.3, 6.5, 15, and 28 GHz) in two scenarios including Urban Micro (UMi) and Outdoor-to-Indoor (O2I) with the same channel sounder. The channel characteristics are extracted and modeled, including path loss (PL), shadow fading, frequency dependence of cluster features, root mean square (RMS) delay spread (DS), Ricean K-factor, and the correlation properties. We mainly focus on the analysis of large-scale parameters and more consideration of the link budget coverage problem. We present the frequency dependence model of the channel characteristics. Among them, in the non-line-of-sight (NLoS) condition, it is found that except for theoretical value brought by higher frequency, additional path loss increment will be generated. Based on these channel characteristics, the impact on performance of the wireless system is analyzed including cell coverage radius, data rate and bit error rate (BER). The results can give an insight into the spectrum selection and optimization in 5G-Advanced and beyond multi-frequency communication systems.

With the commercialization of fifth generation networks worldwide, research into sixth generation (6G) networks has been launched to meet the demands for h

The Ricean K-factor is the ratio of the power of the deterministic multipath component (MPC) and the power of all other stochastic MPCs. The classical moment-based method has been successfully used to estimate the K-factor from time series measurements. In this paper, we apply this method to estimate the K-factor for narrowband temporal selectivity, based upon the analysis of frequency-selectivity in single-snapshot wideband measurements. We also derive the theoretical bias of this estimator and find it depends jointly on the number of channel transfer function envelope samples across the measurement bandwidth, the correlation among such samples, and the K-factor values. Qualitative analysis indicates that the bias increases nearly linearly with the K-factor (K ≥ 1 on the linear scale) and is affected by correlation amongst the samples. Furthermore, the bias is inversely proportional to the number of samples. Simulations confirm the validity of the derivations. Moreover, a measurement campaign is designed at 28 GHz with a system bandwidth of 400 MHz in an urban microcell scenario. The proposed estimator is used to extract the statistics of the K-factor in line of sight and non-line of sight scenarios. The relationship between the K-factor and distance is investigated and a linear model is used to characterize it.