To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
At the forefront of cutting-edge technologies, this text provides a comprehensive treatment of a crucial network performance metric, ushering in new opportunities for rethinking the whole design of communication systems. Detailed exposition of the communication and network theoretic foundations of Age of Information (AoI) gives the reader a solid background, and discussion of the implications on signal processing and control theory shed light on the important potential of recent research. The text includes extensive real-world applications of this vital metric, including caching, the Internet of Things (IoT), and energy harvesting networks. The far-reaching applications of AoI include networked monitoring systems, cyber-physical systems such as the IoT, and information-oriented systems and data analytics applications ranging from the stock market to social networks. The future of this exciting subject in 5G communication systems and beyond make this a vital resource for graduate students, researchers and professionals.
This chapter delves into the problem of wireless-aware path planning for UAVs with a focus on cellular-connected UAV user equipment (UAV UE) that can communicate with ground cellular networks. To this end, we present a very focused study on interference-aware path planning for cellular-connected UAV UEs, in which each UAV aims at achieving a tradeoff between various quality-of-service and mission goals, such as minimizing wireless latency and interference caused on the ground network. To this end, we first motivate the need for wireless-aware path planning for UAV UE and, then, we introduce a rigorous system model for a wireless network with UAV UEs. We then formally pose the wireless-aware path planning problem for UAV UEs using the framework of game theory. We subsequently provide a reinforcement learning solution that can be used to design autonomous, self-organizing wireless-aware path planning mechanisms for UAV UEs while balancing the various wireless and mission objectives of the drones. We also show how some of the unique features of UAVs, such as their altitude and their ability to establish line-of-sight, will have significant impact on the way in which their trajectory is designed.
This chapter focuses on the performance limits and metrics for wireless networks that integrate UAV base stations. In particular, we first have a brief overview on performance analysis techniques such as stochastic geometry. Then, we introduce several detailed case studies to analyze the performance limits of wireless communications with UAVs, while uncovering important design tradeoffs and exposing the impact of various unique UAV features such as altitude, mobility, line-of-sight communications, and elevation angle, on the various metrics.
This chapter investigates a variety of scenarios involving cooperative communications for networks that incorporate UAVs. We particularly analyze the role of cooperative communications in improving the connectivity and capacity of cellular-connected UAV user equipment leveraging principles of coordinated multi-point (CoMP) transmissions among ground base stations. We then study how one can effectively use multiple quadrotor UAVs as an aerial antenna array that acts as a single coordinated UAV base station to provide wireless service to ground users. The goal will be to maximize performance while minimizing the airborne service time for communication. We also characterize the optimal rotor's speed for minimizing the control time using theoretical postulates of bang-bang control theory.
This chapter introduces UAV technology and an in-depth discussion on the wireless communication and networking challenges associated with the introduction of UAVs. This includes providing detailed discussions on UAV classification, UAV regulations, as well as the various UAV use cases in wireless communications (e.g., UAVs as base stations, UAVs as user equipment, and UAVs as relays).
In this chapter, we focus on how wireless communication resources (spectral, temporal, and power) can be optimized and managed in wireless networks that support UAVsWestart by analyzing a very unique problem related to wireless networks supported by hovering UAV base stations: Cell association in hover time constraints. We show how the presence of hover time constraints for the UAVs will drastically change the way in which cell association is performed. Then, we generalize the problem of cell association to a fully fledged 3D cellular system that integrates both UAV base stations and UAV user equipment. Subsequently, we investigate the problem of spectrum and cache management in a wireless network supported by UAV base stations that are able to access both licensed and unlicensed spectrum resources.
This chapter focuses on aerial channel propagation modeling and waveform design for wireless networks with UAVs. We begin by introducing the fundamentals of radio wave propagation and modeling, and, then, we provide an overview of the salient characteristics of aerial wireless channels for UAVs, with a focus on how they differ from the more familiar and well-studied terrestrial wireless channels. Next we characterize large-scale propagation channel effects, including path loss, shadowing, LOS probability, and atmospheric and weather effects, and we discuss the use of ray tracing for UAV channel modelling. We also discuss various small-scale propagation effects. We then turn our attention to waveform design, by reviewing the needed background and providing a small set of exemplary waveforms that showcase the main waveform design considerations for UAV wireless communications and networking.
This chapter studies the problem of UAV deployment for wireless communication purposes. In particular, we focus on the deployment of UAV base stations whose locations will strongly impact the performance that they can deliver. To this end, we start by providing a broad overview on the analytical tools that can be used to develop optimized deployment strategies for wireless networks with UAVs. Then, we investigate how UAV base stations can be deployed for optimizing the wireless coverage for a ground network of wireless devices that seek to communicate with UAV BSs in the downlink. We shed important light on how to deploy the UAV base stations, by determining their number and locations, in a way to maximize network performance, under various constraints, such as power. We then investigate the problem of optimally deploying UAV base stations for collecting data, in the uplink, from ground Internet of Things devices in an energy-efficient manner. We conclude our discussions by studying the deployment of UAV base stations that can leverage machine learning techniques to cache popular content and to track the mobility of ground users.
This chapter is to provide a succinct overview on the security challenges of UAV-based networks. To this end, we start by providing a general overview on the various security threats facing UAV systems, ranging from communication channel attacks to information attacks and Global Positioning System (GPS) spoofing attacks. Then, we develop, using game theory, a generic framework that can provide cyber-physical security for UAV applications such as delivery systems. We conclude with general remarks on the security of UAV systems.
This chapter provides a broad overview on several key applications and use cases of UAVs in various wireless networking scenarios. For the role of a UAV base station, we focus on the use of UAVs in a variety of applications, including public safety, the Internet of Things, caching, edge computing, and smart cities. Then, we discuss a handful of important applications for UAV user equipment, and we show how these applications require UAV users to connect to ground cellular networks. While discussing the various applications, we also provide an in-depth exposition of the associated communications and networking challenges in each application.
This chapter provides a practical discussion on the integration of UAVs into real-world cellular systems, ranging from long-term evolution (LTE) to 5G new radio (NR) and beyond. We first review the roles of mobile cellular technologies for UAV applications while highlighting the use of mobile connectivity and the role of mobile cellular technologies in enabling the development of new services for UAVs in key areas such as identification and registration, location-based services, and law enforcement. Then, we discuss LTE-enabled UAVs in more detail, including a tutorial on LTE and the various UAV use cases that include UAV LTE user equipment and UAV LTE base stations. We also touch upon some performance enhancing solutions that can optimize LTE connectivity for providing improved performance for UAVs while protecting the performance of terrestrial mobile devices. We then introduce various 3GPP standardization efforts on cellular-connected UAVs that aim to address the anticipated usage of mobile technologies by UAVs and regulatory requirements. Next, we discuss 5G NR-enabled UAVs while providing a primer on 5G NR essentials, how 5G NR can provide superior UAV connectivity, and the roles of network slicing and network intelligence for identifying, monitoring, and controlling UAVs in the 5G era.