Air-Assisted Communications Using Line-of-Sight Links

Recently, there has been a rapid increase in the use of air-assisted communications involv-ing the use of airborne platforms such as unmanned aerial vehicles (UAVs). In air-assistednetworks, the UAVs can act like base stations in a traditional cellular network as long as anappropriate backhaul is available. Alternatively, the UAVs could serve as relays, for instance,connecting two ground-based users who are within range of the UAV. UAVs have the benefitof being deployed and reconfigured rapidly and on demand.Meanwhile, there has been a trend towards the use of higher and higher frequencies,including those in millimeter-wave and terahertz bands or even free-space optical communi-cations. Such bands have the benefits of large available bandwidths, relatively little inter-ference, and enhanced security due to spatial isolation. However, such bands are also proneto blocking in the environment, with even relatively small obstacles causing the signal tobe blocked and the link unable to be closed. For such systems, successful communicationrequires a reliable line-of-sight (LoS) link.When using LoS based communications, air-assisted communications is a good solutionbecause the UAVs can be deployed sufficiently high that the ground user will likely have aline of sight or can be maneuvered to create LoS links as needed. This thesis explores theuse of air-assisted communications in cluttered environments with randomized obstructionsthat may block the LoS between the ground user and the air platform. The key challengeis identifying blockages that are taller than a position-dependent critical height that couldblock the LoS of the ground-to-air link. The approach taken is to leverage tools from stochas-tic geometry in general, and Poisson point processes in particular, to derive a closed-formanalytical expression for the probability of obtaining a LoS path in certain environmentscharacterized as Poisson forests. An inhomogeneous Poisson point Process is used to ac-count for the distance-dependence of the critical height, and the LoS probability is the voidprobability of this process. The UAV is assumed to be located at a fixed height, and itshorizontal distance to the ground user could either be fixed or random. Results are verifiedthrough simulation

A Survey on Cellular-connected UAVs: Design Challenges, Enabling 5G/B5G Innovations, and Experimental Advancements

As an emerging field of aerial robotics, Unmanned Aerial Vehicles (UAVs) have gained significant research interest within the wireless networking research community. As soon as national legislations allow UAVs to fly autonomously, we will see swarms of UAV populating the sky of our smart cities to accomplish different missions: parcel delivery, infrastructure monitoring, event filming, surveillance, tracking, etc. The UAV ecosystem can benefit from existing 5G/B5G cellular networks, which can be exploited in different ways to enhance UAV communications. Because of the inherent characteristics of UAV pertaining to flexible mobility in 3D space, autonomous operation and intelligent placement, these smart devices cater to wide range of wireless applications and use cases. This work aims at presenting an in-depth exploration of integration synergies between 5G/B5G cellular systems and UAV technology, where the UAV is integrated as a new aerial User Equipment (UE) to existing cellular networks. In this integration, the UAVs perform the role of flying users within cellular coverage, thus they are termed as cellular-connected UAVs (a.k.a. UAV-UE, drone-UE, 5G-connected drone, or aerial user). The main focus of this work is to present an extensive study of integration challenges along with key 5G/B5G technological innovations and ongoing efforts in design prototyping and field trials corroborating cellular-connected UAVs. This study highlights recent progress updates with respect to 3GPP standardization and emphasizes socio-economic concerns that must be accounted before successful adoption of this promising technology. Various open problems paving the path to future research opportunities are also discussed.Comment: 30 pages, 18 figures, 9 tables, 102 references, journal submissio

A Study of 5G Cellular Connectivity to Unmanned Aerial Vehicles

The market of unmanned aerial vehicles (UAVs) has seen significant growth in the past ten years on both the commercial and military sides. The applications for UAVs are endless and options by manufacturers allow users to modify their drones for their specific goals. This industry has opened up the excitement of piloting vehicles in the air, photography, videography, exploration of nature from a different point of view and many other hobbies assisted by the emergence of UAVs. The growth of this industry coincides with the roll out of new 5G cellular network technology. This upgrade in cellular network infrastructure allows users higher bandwidth, lower latency, more devices per cell and higher reliability. This has created the question, is 5G suited to support UAV activity? Potentially allowing for two-way transmission of images, videos and data between ground users and the unmanned aerial vehicle. There are many challenges that are presented in flying under these communication conditions which need to be explored such as signal reliability, especially in rural areas, the effects of rapidly changing altitudes or velocity of the drone and the effects of antennas that are tuned for terrestrial users. The first of its kind work provided in this thesis, will show results for different UAV experiments on a commercial 5G cellular network in the Clemson, South Carolina area. This is a comprehensive study of both low-band and mid-band 5G cellular coverage relating to UAVs as well as a baseline to existing LTE coverage when available. Featuring first of its kind permission to conduct research on a fully commercial cellular network. This research area is largely new, limited information is currently public on the research into commercial 5G cellular networks supporting UAVs. Other researchers are also starting to collect different key performance indicators (KPIs) for flight signals. Most of their works differ in setup, often using private base stations to give connection, but many of these works will be discussed further in the thesis. LTE and 5G enabled flight allows for a wide variety of applications to use UAVs such as natural disaster assessment, animal poaching surveillance, wild fire detection and prevention, assessing the scene of an accident before police arrive and other more hobby or recreational uses. The end goal is to assure that 5G connection is strong enough to transmit the UAVs real time data, which necessary to help first responders on the ground. When many of the potential uses of a cellular connected UAV are potentially life saving, every second counts and signal needs to be fast, reliable and low latency. Therefore, reliable and high bandwidth communication is necessary for unmanned aerial vehicles to take the next step in real life use cases and to begin to explore the option of beyond visual line of sight (BVLoS) flight and 5G might be the network tools which can get it there

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the recent technological advancements and manufacturing cost reduction opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting their relevance to 5G networks and essentially, how it will play a pivotal role in the development of 6G ecosystem. We discuss important features of NTNs integration into TNs and the synergies by delving into the new range of services and use cases, various architectures, technological enablers, and higher layer aspects pertinent to NTNs integration. Moreover, we review the corresponding challenges arising from the technical peculiarities and the new approaches being adopted to develop efficient integrated ground-air-space (GAS) networks. Our survey further includes the major progress and outcomes from academic research as well as industrial efforts representing the main industrial trends, field trials, and prototyping towards the 6G networks

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the recent technological advancements and manufacturing cost reduction opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting their relevance to 5G networks and essentially, how it will play a pivotal role in the development of 6G ecosystem. We discuss important features of NTNs integration into TNs and the synergies by delving into the new range of services and use cases, various architectures, technological enablers, and higher layer aspects pertinent to NTNs integration. Moreover, we review the corresponding challenges arising from the technical peculiarities and the new approaches being adopted to develop efficient integrated ground-air-space (GAS) networks. Our survey further includes the major progress and outcomes from academic research as well as industrial efforts representing the main industrial trends, field trials, and prototyping towards the 6G networks

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure

AIM: Acoustic Inertial Measurement for Indoor Drone Localization and Tracking

We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR). We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than commercial UWB-based systems in complex indoor scenarios, where state-of-the-art infrared systems would not even work because of NLoS settings. We further demonstrate that AIM can be extended to support indoor spaces with arbitrary ranges and layouts without loss of accuracy by deploying distributed microphone arrays