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.

Massive MIMO is envisioned as a promising technology in 5G and beyond 5G communication. Channel models are of great importance for the development and performance assessment of massive MIMO systems. Since massive MIMO systems are equipped with large-aperture antenna arrays, antenna elements at different spatial positions would observe different channel multipath characteristics, which is so-called spatial non-stationarity (SnS). The SnS property of multipaths has been observed in many reported massive MIMO channel measurements. However, characterization and explanations of SnS have not been adequate in existing statistical channel modeling, and deterministic models (e.g., Ray-tracing) are difficult to implement due to the high complexity. This paper proposes a realistic yet low-complexity SnS channel modeling framework for massive MIMO systems and its validation based on both channel measurements and Ray-tracing simulations. In this work, we firstly perform an 6 GHz-bandwidth millimeter-wave (mmWave) indoor channel measurement campaign with a 0.5 m-radius virtual uniform circular array (UCA), where the SnS phenomena are clearly observed. Then, we propose the massive MIMO channel modeling framework that captures the observed SnS property from physical propagation mechanisms of dominant multipaths in mmWave channels, i.e., blockage, reflection, and diffraction. Compared to traditional stationary channel modeling, only one extra parameter accounting for SnS has been added in the proposed framework, which is desirable for its low-complexity implementation. Finally, the proposed framework is validated with site-specific Ray-tracing simulations. The SnS phenomena observed in the measurements are reproduced well in the modeling results according to the proposed framework, and high similarities between the target channels and modeling results are achieved. The proposed framework is valuable for the development of massive MIMO systems, since it is realistic, low-complexity, and accurate.

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.

Background: Mental fatigue is usually caused by long-term cognitive activities, mainly manifested as drowsiness, difficulty in concentrating, decreased alertness, disordered thinking, slow reaction, lethargy, reduced work efficiency, error-prone and so on. Mental fatigue has become a widespread sub-health condition, and has a serious impact on the cognitive function of the brain. However, seldom studies investigate the differences of mental fatigue on electrophysiological activity both in resting state and task state at the same time. Here, twenty healthy male participants were recruited to do a consecutive mental arithmetic tasks for mental fatigue induction, and electroencephalogram (EEG) data were collected before and after each tasks. The power and relative power of five EEG rhythms both in resting state and task state were analyzed statistically. Results: The results of brain topographies and statistical analysis indicated that mental arithmetic task can successfully induce mental fatigue in the enrolled subjects. The relative power index was more sensitive than the power index in response to mental fatigue, and the relative power for assessing mental fatigue was better in resting state than in task state. Furthermore, we found that it is of great physiological significance to divide alpha frequency band into alpha1 band and alpha2 band in fatigue related studies, and at the same time improve the statistical differences of sub-bands. Conclusions: Our current results suggested that the brain activity in mental fatigue state has great differences in resting state and task state, and it is imperative to select the appropriate state in EEG data acquisition and divide alpha band into alpha1 and alpha2 bands in mental fatigue related researches.

We consider an intelligent reflecting surface (IRS) wireless-powered NOMA Internet-of-Things (IoT) network, where multiple NOMA IoT devices can communicate with access point (AP) with the aid of IRS. Specifically, IoT devices can harvest energy from a nearby appropriative power station (PS) during the wireless energy transfer (WET) phase through the direct links as well as the reflecting links from IRS. Then, the harvested energy can achieve the wireless information transfer (WIT) during the uplink between IoT devices and AP by adopting NOMA protocol. A novel resource allocation scheme is proposed to maximized the sum throughput of the considered system by jointly optimizing the time allocation factor and phase shift matrices of WET and WIT. The optimization problem is non-convex due to multiple variables. We first transfer this problem into convex function by assuming one of the phase shift matrices fixed, and solve the transferred problem by obtaining closed-form solution. Then, the elements collaborative approximate (ECA) and the manifold space gradient descent (MSGD) algorithms are designed to optimize the phase shift matrix. ECA is iteratively optimizing one element and MSGD is focus on the derivation of a gradient descent over the manifold space. Numerical results indicate the sum throughput enhancement by IRS compared with no IRS, and highlight the advantages of IRS.