The fifth generation (5G) mobile communication systems will be in use around 2020. The aim of 5G systems is to provide anywhere and anytime connectivity for anyone and anything. Several new technologies are being researched for 5G systems, such as massive multiple-input multiple-output (MIMO) communications, vehicle-to-vehicle (V2V) communications, high-speed train (HST) communications, and millimeter wave (mmWave) communications. Each of these technologies introduces new propagation properties and sets specific requirements on 5G channel modeling. Considering the fact that channel models are indispensable for system design and performance evaluation, accurate and efficient channel models covering various 5G technologies and scenarios are urgently needed. This paper first summarizes the requirements of the 5G channel modeling, and then provides an extensive review of the recent channel measurements and models. Finally, future research directions for channel measurements and modeling are provided.

Optical wireless communications (OWCs) refer to wireless communication technologies which utilize optical carriers in infrared, visible light, or ultraviolet bands of electromagnetic spectrum. For the sake of an OWC link design and performance evaluation, a comprehensive understanding and an accurate prediction of link behavior are indispensable. Therefore, accurate and efficient channel models are crucial for the OWC link design. This paper first provides a brief history of OWCs. It also considers OWC channel scenarios and their utilization trade-off in terms of optical carrier, range, mobility, and power efficiency. Furthermore, the main optical channel characteristics that affect the OWC link performance are investigated. A comprehensive overview of the most important OWCs channel measurement campaigns and channel models, primarily for wireless infrared communications and visible light communications, are presented. OWCs channel models are further compared in terms of computation speed, complexity, and accuracy. The survey considers indoor, outdoor, underground, and underwater communication environments. Finally, future research directions in OWCs channel measurements and models are addressed.

In order to provide reliable and efficient connections between unmanned aerial vehicles (UAVs) and ground stations (GSs), realistic UAV-to-ground channel models are indispensable. In this article, we propose a novel 3-D nonstationary geometry-based stochastic model (GBSM) for UAV-to-ground multiple-input-multiple-output (MIMO) channels. Distinctive UAV-to-ground channel characteristics, such as time-domain nonstationarity, distinctions between different altitudes, spatial consistency, and 3-D arbitrary UAV movement trajectories, are taken into account. By adjusting parameter settings, the proposed channel model framework is sufficiently general to support multiple frequency bands and multiple scenarios, including millimeter wave (mmWave) and massive MIMO configurations. Statistical properties, including power delay profile (PDP), stationary interval, space-time correlation function (STCF), and root-mean-square (RMS) delay spread are derived and analyzed for different frequencies and scenarios. The accuracy of the proposed model is validated by comparing its statistical properties with corresponding available channel measurements. The proposed channel model will provide a fundamental support for the design, performance evaluation, and optimization of future UAV integrated sixth-generation (6G) wireless networks.

In this paper, the new enhanced cognitive radio networks (E-CRNs) based on spectrum sharing (SS) and spectrum aggregation (SA) are proposed for the fifth generation (5G) wireless networks. The E-CRNs jointly exploit the licensed spectrum shared with the primary user (PU) networks and the unlicensed spectrum aggregated from the industrial, scientific, and medical (ISM) bands. The PU networks include TV systems in TV white space (TVWS) and different incumbent systems in the long term evolution (LTE) time division duplexing (TDD) bands. The harmful interference from the E-CRNs to the PU networks are delicately controlled. Furthermore, the coexistence between the E-CRNs and other unlicensed systems, such as WiFi, is studied. The E-CRNs framework including dynamic spectrum management (DSM) is designed for the key parameters of licensed SS and unlicensed SA. The essential tradeoff between sharing efficiency (ShE) and aggregation efficiency (AgE) for the E-CRNs is discussed. Based on this tradeoff, a spectrum lean-management (SLM) scheme is proposed to fulfill the DSM. Moreover, a water-filling (WF) algorithm is designed to dynamically access the available spectrum. Numerical results demonstrate that the proposed E-CRNs can significantly improve the system performance in terms of data rate, outage probability, and spectrum efficiency (SE). In particular, the ECRNs framework provides a spectrum usage prototype for 5G wireless communication networks.

In fifth generation (5G) wireless communications, high-speed train (HST) communications is one of the most challenging scenarios. By adopting massive multiple-input multiple-output (MIMO) and millimeter wave (mmWave) technologies into HST communications, the underlying communication system design becomes more challenging and some new channel characteristics have to be studied, such as the non-stationarities in space, time, and frequency domains. This paper proposes a novel three-dimensional space-time-frequency non-stationary mmWave massive MIMO theoretical model, as well as a corresponding simulation model for 5G HST wireless channels based on WINNER II and Saleh-Valenzuela channel models. Cluster evolutions in space, time, and frequency domains are proposed and analyzed to ensure the models' non-stationarities in three domains. Moreover, based on the proposed channel models, important time-variant channel statistical properties are investigated, such as the time autocorrelation function, space cross-correlation function, delay power spectrum density (PSD), angular PSD, and frequency correlation function. Results indicate that the statistical properties of the simulation model, verified by simulation results, can match well with those of the theoretical model.