A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

In recent years, there has been a dramatic increase in the use of unmanned aerial vehicles (UAVs), particularly for small UAVs, due to their affordable prices, ease of availability, and ease of operability. Existing and future applications of UAVs include remote surveillance and monitoring, relief operations, package delivery, and communication backhaul infrastructure. Additionally, UAVs are envisioned as an important component of 5G wireless technology and beyond. The unique application scenarios for UAVs necessitate accurate air-to-ground (AG) propagation channel models for designing and evaluating UAV communication links for control/non-payload as well as payload data transmissions. These AG propagation models have not been investigated in detail when compared to terrestrial propagation models. In this paper, a comprehensive survey is provided on available AG channel measurement campaigns, large and small scale fading channel models, their limitations, and future research directions for UAV communication scenarios

Air-to-Ground Channel Characterization for Low-Height UAVs in Realistic Network Deployments

Due to the decrease in cost, size and weight, \acp{UAV} are becoming more and more popular for general-purpose civil and commercial applications. Provision of communication services to \acp{UAV} both for user data and control messaging by using off-the-shelf terrestrial cellular deployments introduces several technical challenges. In this paper, an approach to the air-to-ground channel characterization for low-height \acp{UAV} based on an extensive measurement campaign is proposed, giving special attention to the comparison of the results when a typical directional antenna for network deployments is used and when a quasi-omnidirectional one is considered. Channel characteristics like path loss, shadow fading, root mean square delay and Doppler frequency spreads and the K-factor are statistically characterized for different suburban scenarios.Comment: 15 pages, accepted in IEEE Transactions on Antennas and Propagatio

Unmanned aerial vehicles (UAVs) for wireless communication and networks : potentials and design challenges

Unmanned aerial vehicles (UAVs) are mostly considered by the military for surveillance and reconnaissance operations, and by hobbyists for aerial photography. However, in recent years, the UAV operations have been extended for civilian and commercial purposes due to their agile and cost-effective deployment. UAVs appear to be more prolific platforms to enable wireless communication due to their better line-of-sight (LOS) channel conditions as compared with the fixed base stations (BSs) in terrestrial communication which suffer from severe path loss, shadowing, and multipath fading in more challenging propagation environments. In UAV-enabled wireless communications, the UAV can either act as a complementary aerial BS to provide on-demand communication or as an aerial user equipment (UE) which is operated by the existing cellular network. Several challenges exist in the design of UAV communications which include but not limited to channel modeling, optimal deployment, interference generation, performance analysis, limited on-board battery lifetime, trajectory optimization, and unavailability of regulations and standards which are specific for UAV communication and networking. This thesis particularly investigates some important design challenges for safe and reliable functionalities of UAV for wireless communication and networking. UAV communication has its own distinctive channel characteristics compared to the widely used cellular or satellite systems. However, several challenges exist in UAV channel modeling. For example, the propagation characteristics of UAV channels are under explored for spatial and temporal variations in non-stationary channels. Therefore, first and foremost, this thesis provides an extensive review of the measurement methods proposed for UAV channel modeling and discusses channel modeling efforts for air-to-ground and air-to-air channels. Furthermore, knowledge-gaps are identified to realize accurate UAV channel models. The efficient deployment strategy is imperative to compensate the adverse impact of interference on the coverage area performance of multiple UAVs. As a result, this thesis proposes an optimal deployment strategy for multiple UAVs in presence of downlink co-channel interference in the worst-case scenario. In particular, this work presents coordinated multi-UAV strategy in two schemes. In the first scheme, symmetric placement of UAVs is assumed at a common optimal altitude and transmit power. In the second scheme, asymmetric deployment of UAVs with different altitudes and transmit powers is assumed. The impact of various system parameters, such as signal-to interference-plus-noise ratio (SINR) threshold, separation distance between UAVs, and the number of UAVs and their formations are carefully studied to achieve the maximum coverage area inside and to reduce the unnecessary coverage expansion outside the target area. Fundamental analysis is required to obtain the optimal trade-off between the design parameters and performance metrics of any communication systems. This thesis particularly considers two emerging scenarios for evaluating performance of UAV communication systems. In the first scenario, the uplink UAV communication system is considered where the ground user follows the random waypoint (RWP) model for user mobility, the small-scale channel fading follows the Nakagami-m model, and the uplink interference is modeled by Gamma approximation. Specifically, the closed-form expressions for the probability density function (PDF), the cumulative distribution function (CDF), the outage probability, and the average bit error rate (BER) of the considered UAV system are derived as performance metrics. In the second scenario, the downlink hybrid caching system is considered where UAVs and ground small-cell BSs (SBSs) are distributed according to two independent homogeneous Poisson point processes (PPPs), and downlink interference is modeled by the Laplace transforms. Specifically, the analytical expressions of the successful content delivery probability and energy efficiency of the considered network are derived as performance metrics. In both scenarios, results are presented to demonstrate the interplay between the communication performance and the design parameters

Modelling and coverage improvement of DVB-T networks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe necessity of accurate point-to-area and point-to-point prediction tools arises from the enormous demand in designing broadcasting systems for digital TV and cellular communications. Up to now, a considerable number of coverage prediction models for radio coverage has been developed. In electromagnetic wave propagation theory, there are three types of propagation models. Empirical models that are based on a large quantity of measurement data are elementary but not very accurate. Semi-deterministic models that are based on measurement data and electromagnetic theory of propagation, which are more precise. Finally, deterministic models based on theoretical physics, like diffraction theory and Fresnel theory, that require a significant amount of geometrical data about the propagation terrain profile but are the most accurate. The primary outcomes of this research are the comparative study and improvement of several propagation models, using a significant quantity of measurements and simulations and the deduction of useful conclusions to be used by engineers to improve propagation predictions further. In this research, the Longley-Rice (ITM) Irregular Terrain Model model was used, a classic model used for TV coverage prediction, which model is to date the preferred model of the FCC (Federal Communications Commission) in the US for FM-TV coverage calculations. To run the model, the Radio Mobile program (Radio Propagation and Virtual Mapping Freeware) was used based on the Longley-Rice Model ITM, including the 3-arc-second Satellite Radar Terrain Mission (SRTM) maps and the SPLAT! program (an RF Signal Propagation, Loss, And Terrain analysis tool), which also relies on the Longley-Rice ITM model and makes use of SRTM maps. Both programs work in Windows operating system (Windows7 Professional, 64 bits). Another model used in this research was SPLAT! with ITWOM (Irregular Terrain with Obstructions Model) which combines empirical data from the ITU-R P.1546 model and other ITU recommendations in conjunction with Beer's and Snell's laws. The ITU-R Recommendation P.1546 model and the empirical Hata-Davidson model using HAAT were also utilized in this research. The Single Knife-Edge (SKE) model was coded in MATLAB and utilized in this research as a simple reference model, where only one main obstacle is considered. Other well-known multiple knife-edge diffraction models employed in this study are the Epstein-Peterson, Deygout, and Giovaneli models. For these deterministic models, individual MATLAB programs were written. Simulations produced by the models were limited to the main two knife-edges of the propagation path for immediate comparison with the Longley-Rice model which uses the “double knife-edge” approach. All measurement campaigns took place in Northern Greece and Southern (F.Y.R.O.M) Former Yugoslav Republic of Macedonia using a Rohde & Schwarz FSH-3 portable spectrum analyser and precision calibrated antennas

Artificial neural networks for location estimation and co-cannel interference suppression in cellular networks

This thesis reports on the application of artificial neural networks to two important problems encountered in cellular communications, namely, location estimation and co-channel interference suppression. The prediction of a mobile location using propagation path loss (signal strength) is a very difficult and complex task. Several techniques have been proposed recently mostly based on linearized, geometrical and maximum likelihood methods. An alternative approach based on artificial neural networks is proposed in this thesis which offers the advantages of increased flexibility to adapt to different environments and high speed parallel processing. Location estimation provides users of cellular telephones with information about their location. Some of the existing location estimation techniques such as those used in GPS satellite navigation systems require non-standard features, either from the cellular phone or the cellular network. However, it is possible to use the existing GSM technology for location estimation by taking advantage of the signals transmitted between the phone and the network. This thesis proposes the application of neural networks to predict the location coordinates from signal strength data. New multi-layered perceptron and radial basis function based neural networks are employed for the prediction of mobile locations using signal strength measurements in a simulated COST-231 metropolitan environment. In addition, initial preliminary results using limited available real signal-strength measurements in a metropolitan environment are also reported comparing the performance of the neural predictors with a conventional linear technique. The results indicate that the neural predictors can be trained to provide a near perfect mapping using signal strength measurements from two or more base stations. The second application of neural networks addressed in this thesis, is concerned with adaptive equalization, which is known to be an important technique for combating distortion and Inter-Symbol Interference (ISI) in digital communication channels. However, many communication systems are also impaired by what is known as co-channel interference (CCI). Many digital communications systems such as digital cellular radio (DCR) and dual polarized micro-wave radio, for example, employ frequency re-usage and often exhibit performance limitation due to co-channel interference. The degradation in performance due to CCI is more severe than due to ISI. Therefore, simple and effective interference suppression techniques are required to mitigate the interference for a high-quality signal reception. The current work briefly reviews the application of neural network based non-linear adaptive equalizers to the problem of combating co-channel interference, without a priori knowledge of the channel or co-channel orders. A realistic co-channel system is used as a case study to demonstrate the superior equalization capability of the functional-link neural network based Decision Feedback Equalizer (DFE) compared to other conventional linear and neural network based non-linear adaptive equalizers.This project was funded by Solectron (Scotland) Ltd