Autonomous Airborne Wireless Networks. Группа авторов
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flight zone. Hence, developing new signal processing, communication, and optimization framework for autonomous airborne wireless networks is essential. Such networks can offer high data rates and assist the traditional terrestrial networks to provide real‐time and ultrareliable sensing applications for the beyond‐5G networks. Achieving this gain requires the correct characterization of the propagation channel while considering the high mobility dynamics. Accurate channel modeling is imperative to fulfill the ever‐increasing requirements of the end user to transfer data at higher rates. The air‐to‐ground (AG) and the air‐to‐air (AA) channel propagation models for the airborne wireless network channel can be characterized by using measurement and empirical studies. Further, the key performance indicators (KPIs) of airborne wireless networks such as flight time, trajectory, data rate, energy efficiency, and latency need to be optimized for the different use cases.
This book explores recent advances in the theory and practice of airborne wireless networks for the next generation of wireless networks to support various applications, including emergency communications, coverage and capacity expansion, Internet of things (IoT), information dissemination, future healthcare, pop‐up networks, etc. The book focuses on channel characteristics and modeling, networking architectures, self‐organized airborne networks, self‐organized backhaul, artificial‐intelligence‐enabled trajectory optimization, and application in sectors such as agriculture, underwater communications, and emergency networks. The book further highlights the main considerations during the design of the autonomous airborne networks and exploits new opportunities due to the recent advancement in wireless communication systems.
This book for the first time evaluates the advances in the current state of the art and it provides readers with insights on how airborne wireless networks can seamlessly support various applications expected in future networks. More specifically, the book shows the readers how the integration of self‐organized networks and artificial intelligence can support the various use cases of airborne wireless networks.
UAVs provide a suitable aerial platform for various wireless network applications that require reliable and ubiquitous communication. The channel model plays a crucial role in the wireless communications system and thus Chapter 2 focuses on the channel model for UAV networks. The authors first provide an overview of UAV networks in terms of their classification and how they can be used to enable future wireless communication systems. Accurate channel modeling is imperative to fulfill the ever‐increasing requirements of the end user to transfer data at higher rates. Hence, the authors discuss channel modeling in UAV communications while focusing on the salient feature of the AG and AA propagation channels. Finally, the chapter concludes by discussing some of the key research challenges for the practical deployment of UAVs as airborne wireless nodes.
In Chapter 3, the authors describe the fundamental properties of the ultrawide band (UWB) channel and present one of the first experimental off‐body studies between a human subject and an UAV at 7.5 GHz of bandwidth. In the study presented in this chapter, the transmitter antenna was placed on a UAV while the receiver antenna was patched on a human subject at different body locations during the campaign. The chapter presents the measurement setting, detailing the measurement campaign that was conducted in an indoor and an outdoor environment with LoS and non‐line‐of‐sight (NLoS) cases. Furthermore, the chapter presents the UWB‐unmanned aerial vehicle‐to‐wearables (UAV2W) channel characterization. Finally, the chapter presents the statistical analysis to determine the distribution that best characterizes the fading channels between different body locations and the UAV.
Chapter 4 describes the use of a Q‐learning algorithm, which is based on a cooperative multiagent approach, to intelligently find the optimal position of a set of drones. The algorithm presented in the chapter is designed with the objective to minimize the number of users in an outage in the network. Hence, the algorithm determines the optimal distribution of frequencies and whether it should shut down a set of drones. The chapter also proposes and compares four different strategies for the Q‐learning algorithm with different action selection policies, whose algorithms differ in terms of design complexity, ability to vary the number of drones in operation, and convergence time. The chapter presents numerical results that show the relationship between the density of users in the region of interest and the number of frequencies in operation.
In Chapter 5, the authors describe a self‐energized UAV‐assisted caching relaying scheme. In this scheme, the UAV's communication capabilities are powered solely by the power‐splitting simultaneous wireless information and power transfer (PS‐SWIPT) energy‐harvesting (EH) technique, and it employs decode and forward (DF) relaying protocol to assist the information transmission to users from the source node. The authors present the transmission block diagram to accommodate communication processes within the system. Afterward, the authors address the problem of identifying optimal time and energy resources for the communication system and the optimal UAV's trajectory while adhering to the quality of service (QoS) requirements of the communication network. Finally, numerical simulation results to identify the impacts of the system parameters on the information rate at the user equipment are presented.
Chapter 6 focuses on the case study of millimeter‐wave (mmWave) and terahertz (THz) communication and technical challenges for applying mmWave and THz frequency band for communication with UAVs. The chapter starts by presenting the potential of mmWave and THz bands for communications. This is followed by an overview of the technical challenges for implementing mmWave and THz band for UAV communications. The chapter then presents a theoretical analysis that focuses on the placement of UAVs. Besides, the chapter investigates the performance of UAV‐enabled hybrid heterogeneous network (HetNet) by considering stringent communication‐related constraints such as the system bandwidth, data rate, signal‐to‐noise ratio (SNR), etc. The association of terrestrial small‐cell base stations (SCBs) with UAVs is addressed such that the sum rate of the overall system is maximized. Finally, numerical results are included to show the favorable performance of the UAV‐assisted wireless network.
In Chapter 7, the authors discuss a method that uses a cooperative UAV as a friendly jammer to enhance the security performance of cognitive radio networks. The chapter starts by presenting the system model for the UAV‐enabled cooperative jamming in a cognitive radio system. Then the optimization problem is formulated. The resource allocation in the network must jointly optimize the transmission power and UAV's trajectory to maximize the secrecy rate while satisfying a given interference threshold at the primary receiver (PR). With the original problem non‐convex, the authors first transform the original problem into a more tractable form and then present a successive convex approximation‐based algorithm for its solutions. Finally, numerical results are included to show a significant improvement in the security performance of the UAV‐enabled cognitive radio networks.
Chapter 8 explores the possibility of using intelligent reflecting surfaces (IRS) in airborne networks for the localization of users and base stations. Positioning is an important aspect in the present and future wireless networks, where it augments the network operations and assists in multiple localization‐based applications. The chapter starts by presenting the related works and the underlying opportunities around IRS‐ and UAV‐based base stations. The authors then discuss the integration of IRS in ANs and the potential use cases. Afterward, the chapter presents an IRS‐based localization model for ANs along with some mathematical modeling. Finally, some future research challenges that present research opportunities are included.
Chapter 9 describes the application of UAVs for disaster recovery networks. The chapter starts by providing an overview of the UAV networks including the description of the UAV architectures, namely, single‐UAV systems, multi‐UAV systems, cooperative multi‐UAV systems, and multilayer UAV networks.