The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. On the other hand, UAVs can operate as flying mobile terminals within a cellular network. Such cellular-connected UAVs can enable several applications ranging from real-time video streaming to item delivery. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as 3D deployment, performance analysis, channel modeling, and energy efficiency are explored along with representative results. Then, open problems and potential research directions pertaining to UAV communications are introduced. Finally, various analytical frameworks and mathematical tools, such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.

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A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems

Massive multiple-input multiple-output MIMO is one of themost promising technologies for the next generation of wirelesscommunication networks because it has the potential to providegame-changing improvements in spectral efficiency SE and energyefficiency EE. This monograph summarizes many years ofresearch insights in a clear and self-contained way and providesthe reader with the necessary knowledge and mathematical toolsto carry out independent research in this area. Starting froma rigorous definition of Massive MIMO, the monograph coversthe important aspects of channel estimation, SE, EE, hardwareefficiency HE, and various practical deployment considerations.From the beginning, a very general, yet tractable, canonical systemmodel with spatial channel correlation is introduced. This modelis used to realistically assess the SE and EE, and is later extendedto also include the impact of hardware impairments. Owing tothis rigorous modeling approach, a lot of classic "wisdom" aboutMassive MIMO, based on too simplistic system models, is shownto be questionable.

https://www.nowpublishers.com/article/DownloadSummary/SIG-093

Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency

The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. On the other hand, UAVs can operate as flying mobile terminals within a cellular network. Such cellular-connected UAVs can enable several applications ranging from real-time video streaming to item delivery. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as three-dimensional deployment, performance analysis, channel modeling, and energy efficiency are explored along with representative results. Then, open problems and potential research directions pertaining to UAV communications are introduced. Finally, various analytical frameworks and mathematical tools such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.

Channel models are important tools to evaluate the performance of new concepts in mobile communications. However, there is a tradeoff between complexity and accuracy. In this paper, we extend the popular Wireless World Initiative for New Radio (WINNER) channel model with new features to make it as realistic as possible. Our approach enables more realistic evaluation results at an early stage of algorithm development. The new model supports 3-D propagation, 3-D antenna patterns, time evolving channel traces of arbitrary length, scenario transitions and variable terminal speeds. We validated the model by measurements in a coherent LTE advanced testbed in downtown Berlin, Germany. We then reproduced the same scenario in the model and compared several channel parameters (delay spread, path gain, K-factor, geometry factor and capacity). The results match very well and we can accurately predict the performance for an urban macro-cell setup with commercial high-gain antennas. At the same time, the computational complexity does not increase significantly and we can use all existing WINNER parameter tables. These artificial channels, having equivalent characteristics as measured data, enable virtual field trials long before prototypes are available.

QuaDRiGa: A 3-D Multi-Cell Channel Model With Time Evolution for Enabling Virtual Field Trials

Fourth-generation (4G) systems are expected to support data rates of the order of 100 Mb/s in the outdoor environment and 1 Gb/s in the indoor/stationary environment. In order to support such large payloads, the radio physical layer must employ receiver algorithms that provide a significant increase in spectrum efficiency (and, hence, capacity) over current wireless systems. Recently, an explosion of multiple-input-multiple-output (MIMO) studies have appeared with many journals presenting special issues on this subject. This has occurred due to the potential of MIMO to provide a linear increase in capacity with antenna numbers. Environmental considerations and tower loads will often restrict the placing of large antenna spans on base stations (BSs). Similarly, customer device form factors also place a limit on the antenna numbers that can be placed with a mutual spacing of 0.5 wavelength. The use of cross-polarized antennas is widely used in modern cellular installations as it reduces spacing needs and tower loads on BSs. Hence, this approach is also receiving considerable attention in MIMO systems. In order to study and compare various receiver architectures that are based on MIMO techniques, one needs to have an accurate knowledge of the MIMO channel. However, very few studies have appeared that characterize the cross-polarized MIMO channel. Recently, the third-generation partnership standards bodies (3GPP/3GPP2) have defined a cross-polarized channel model for MIMO systems but this model neglects the elevation spectrum. In this paper, we provide a deeper understanding of the channel model for cross-polarized systems for different environments and propose a composite channel impulse model for the cross-polarized channel that takes into account both azimuth and elevation spectrum. We use the resulting channel impulse response to derive closed-form expressions for the spatial correlation. We also present models to describe the dependence of cross-polarization discrimination (XPD) on distance, azimuth and elevation and delay spread. In addition, we study the impact of array width, signal-to-noise ratio, and antenna slant angle on the mutual information (MI) of the system. In particular, we present an analytical model for large system mean mutual information values and consider the impact of elevation spectrum on MI. Finally, the impact of multipath delays on XPD and MI is also explored.

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Polarized MIMO channels in 3-D: models, measurements and mutual information

Many channel models for MIMO systems have appeared in the literature. However, with the exception of a few recent results, they are largely focussed on two dimensional (2D) propagation, i.e., propagation in the horizontal plane, and the impact of elevation angle is not considered. The assumption of 2D propagation breaks down when in some propagation environments the elevation angle distribution is significant. Consequently, the estimation of ergodic capacity assuming a 2D channel coefficient alone can lead to erroneous results. In this paper, for cross polarized channels, we define a composite channel model and channel coefficient that takes into account both 2D and 3D propagation. Using this composite channel coefficient we assess the ergodic channel capacity and discuss its sensitivity to a variety of different azimuth and elevation power distributions and other system parameters.

/pdf/the-impact-of-elevation-angle-on-mimo-capacity-3btgi4zy0g.pdf

The Impact of Elevation Angle on MIMO Capacity

In this paper we develop a multiple input multiple output (MIMO) channel model and derive its spatial and temporal correlation properties. We present a generalized methodology to derive the spatial correlation when the angles of arrival (AoA) and angles of departure (AoD) are either independent or partly correlated. Our model therefore spans the full range from well-established single ring models, where AoA and AoD are fully correlated to complex industrial channel models where they are uncorrelated. It is shown that first order and second order approximations to the channel give rise to a single- Kroneckermodel and a sum-Kroneckermodel respectively. We compare our model to other MIMO channel models in terms of correlation structure and the ergodic mutual information (EMI) and study the effect of the non-Kronecker correlation structure.

/pdf/an-extended-one-ring-mimo-channel-model-277wugk331.pdf

An Extended One-Ring MIMO Channel Model

In this paper we develop a MIMO channel model and derive its spatial and temporal correlation properties. We present a generalized methodology to derive the spatial correlation when the angles of arrival (AoA) and angles of departure are either independent or fully correlated. Our model therefore spans the full range from well-established single ring models, where AoA and AoD are fully correlated to complex industrial channel models where they are uncorrelated. We compare our model to other MIMO channel models in terms of correlation structure and mutual information. Finally, it is shown that first order and second order approximations to the channel give rise to a single-Kronecker model and a sum-Kronecker model respectively

/pdf/a-new-space-time-mimo-channel-model-1zo4epn3xt.pdf

A new space-time MIMO channel model

This paper presents a novel matched rotation precoding (MRP) scheme to design a rate one space-frequency block code (SFBC) and a multirate SFBC for MIMO-OFDM systems with limited feedback. The proposed rate one MRP and multirate MRP can always achieve full transmit diversity and optimal system performance for arbitrary number of antennas, subcarrier intervals, and subcarrier groupings, with limited channel knowledge required by the transmit antennas. The optimization process of the rate one MRP is simple and easily visualized so that the optimal rotation angle can be derived explicitly, or even intuitively for some cases. The multirate MRP has a complex optimization process, but it has a better spectral efficiency and provides a relatively smooth balance between system performance and transmission rate. Simulations show that the proposed SFBC with MRP can overcome the diversity loss for specific propagation scenarios, always improve the system performance, and demonstrate flexible performance with large performance gain. Therefore the proposed SFBCs with MRP demonstrate flexibility and feasibility so that it is more suitable for a practical MIMO-OFDM system with dynamic parameters.

/pdf/space-frequency-block-code-with-matched-rotation-for-mimo-4ofpllhp5o.pdf

Min Zhang

Papers

Polarized MIMO channels in 3-D: models, measurements and mutual information

The Impact of Elevation Angle on MIMO Capacity

An Extended One-Ring MIMO Channel Model

A new space-time MIMO channel model

Space-frequency block code with matched rotation for MIMO-OFDM system with limited feedback