Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight (LoS) connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication towards 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space-air-ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains including physical layer, network layer, and joint communication, computing and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions.

UAV Communications for 5G and Beyond: Recent Advances and Future Trends

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

In recent years, unmanned aerial vehicles (UAVs) have received considerable attention from regulators, industry and research community, due to rapid growth in a broad range of applications. Particularly, UAVs are being used to provide a promising solution to reliable and cost-effective wireless communications from the sky. The deployment of UAVs has been regarded as an alternative complement of existing cellular systems, to achieve higher transmission efficiency with enhanced coverage and capacity. However, heavily utilized microwave spectrum bands below 6 GHz utilized by legacy wireless systems are insufficient to attain remarkable data rate enhancement for numerous emerging applications. To resolve the spectrum crunch crisis and satisfy the requirements of 5G and beyond mobile communications, one potential solution is to use the abundance of unoccupied bandwidth available at millimeter wave (mmWave) frequencies. Inspired by the technique potentials, mmWave communications have also paved the way into the widespread use of UAVs to assist wireless networks for future 5G and beyond wireless applications. In this paper, we provide a comprehensive survey on current achievements in the integration of 5G mmWave communications into UAV-assisted wireless networks. More precisely, a taxonomy to classify the existing research issues is presented, by considering seven cutting-edge solutions. Subsequently, we provide a brief overview of 5G mmWave communications for UAV-assisted wireless networks from two aspects, i.e., key technical advantages and challenges as well as potential applications. Based on the proposed taxonomy, we further discuss in detail the state-of-the-art issues, solutions, and open challenges for this newly emerging area. Lastly, we complete this survey by pointing out open issues and shedding new light on future directions for further research on this area.

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A Survey on 5G Millimeter Wave Communications for UAV-Assisted Wireless Networks

UAV assistance paradigm: State-of-the-art in applications and challenges

Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Vehicles (IoV) components enable effective Artificial Intelligence (AI) based models to be built. Beyond ground data sources, Unmanned Aerial Vehicles (UAVs) based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), is becoming increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impedes data sharing across independently owned UAVs. To this end, we propose the adoption of a Federated Learning (FL) based approach to enable privacy-preserving collaborative Machine Learning across a federation of independent DaaS providers for the development of IoV applications, e.g., for traffic prediction and car park occupancy management. Given the information asymmetry and incentive mismatches between the UAVs and model owners, we leverage on the self-revealing properties of a multi-dimensional contract to ensure truthful reporting of the UAV types, while accounting for the multiple sources of heterogeneity, e.g., in sensing, computation, and transmission costs. Then, we adopt the Gale-Shapley algorithm to match the lowest cost UAV to each subregion. The simulation results validate the incentive compatibility of our contract design, and shows the efficiency of our matching, thus guaranteeing profit maximization for the model owner amid information asymmetry.

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Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach

The next generation of mobile systems, 5G, will be the communication standard that accommodates the proliferation of the Internet of Things (IoT). Unmanned aerial vehicles (UAVs) are envisioned to support many applications in providing 5G connectivity to the IoT, by extending highspeed connectivity from the sky to objects on the ground, or even by carrying onboard some IoT devices. However, given their critical nature, the management of UAVs induces high exchange of control messages with the ground control station, resulting in the crowded spectrum used by cellular networks. The authors raise the problem of degrading the network spectrum with UAVs' management messages, and discuss the need for an efficient orchestration system. In this article, they propose a novel scheme, dubbed the aerial control system, which is based on separating the data plane from the control plane of UAVs, and pushing the latter to be performed in the air by UAVs. The solution provides an orchestration logic that takes advantage of the autonomous nature of UAVs to organize UAVs in one or several clusters. UAV-to-UAV communication enables spectrum reuse and avoids crowding the network with management messages, while dedicating more 5G spectrum for ensuring more bandwidth to the IoT through UAV-to-infrastructure communication.

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Aerial Control System for Spectrum Efficiency in UAV-to-Cellular Communications

Given the continuously increasing use of Unmanned Aerial Vehicles (UAVs) in different domains, their management in the uncontrolled airspace has become a necessity. This has given rise to new systems called UAVs Traffic Management (UTM) systems. Nevertheless, currently, there is a lack of communication infrastructures that can support the requirements of UTM systems. Luckily, the envisioned 5G mobile network has introduced the concept of Multi-access Edge Computing (MEC) in its architecture to support mission-critical applications by decreasing the end-to-end latency and the unreliability of communication. In this paper, we evaluate the impact of the network latency and reliability on the control of UAVs' flights. The obtained results show that a UAV can deviate from its intended path with more than $5m$ if the network latency exceeds 400ms and with more than $2m$ if the packet loss probability exceeds 0.2. To overcome these limitations, we have leveraged MEC to provide a new UTM framework that enables an efficient traffic management. Moreover, due to MEC resource-limited nature and in order to give an insight about the resource provisioning, we have evaluated the scalability of the proposed solution in terms of the number of UAVs that can be handled without affecting the efficiency of the proposed UTM framework.

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UAVs Traffic Control Based on Multi-Access Edge Computing

This article provides an overview of enhanced network services, while emphasizing the role of UAVs as core network equipment with radio and backhaul capabilities. Initially, we elaborate the various deployment options, focusing on UAVs as airborne radio, backhaul and core network equipment, pointing out the benefits and limitations. We then analyze the required enhancements in the SBA to support UAV services including UAV navigation and air traffic management, weather forecasting and UAV connectivity management. The use of airborne UAVs network services is assessed via qualitative means, considering the impact on vehicular applications. Finally, an evaluation has been conducted via a testbed implementation, to explore the performance of UAVs as edge cloud nodes, hosting an ACS function responsible for the control and orchestration of a UAV fleet.

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A Service-Based Architecture for Enabling UAV Enhanced Network Services

The increased use of Unmanned Aerial Vehicles (UAVs) in numerous domains, will result in high traffic den- sities in the low-altitude airspace. Consequently, UAVs Traffic Management (UTM) systems that allow the integration of UAVs in the low-altitude airspace are gaining a lot of momentum. Furthermore, the 5th generation of mobile networks (5G) will most likely provide the underlying support for UTM systems by providing connectivity to UAVs, enabling the control, track- ing and communication with remote applications and services. However, UAVs may need to communicate with services with different communication Quality of Service (QoS) requirements, ranging form best-effort services to Ultra-Reliable Low- Latency Communications (URLLC) services. Indeed, 5G can ensure efficient Quality of Service (QoS) enhancements using new technologies, such as network slicing and Multi- access Edge Computing (MEC). In this context, Network Functions Virtual- ization (NFV) is considered as one of the pillars of 5G systems, by providing a QoS-aware Management and Orchestration (MANO) of softwarized services across cloud and MEC platforms. The MANO process of UAVâ€™s services can be enhanced further using the information provided by the UTM system, such as the UAVsâ€™ flight plans. In this paper, we propose an extended framework for the management and orchestration of UAVsâ€™services in MEC- NFV environment by combining the functionalities provided by the MEC-NFV management and orchestration framework with the functionalities of a UTM system. Moreover, we propose an Integer Linear Programming (ILP) model of the placement scheme of our framework and we evaluate its performances. The obtained results demonstrate the effectiveness of the proposed solutions in achieving its design goals.

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Toward a UTM-Based Service Orchestration for UAVs in MEC-NFV Environment

To provide efficient, safe, and secure access to the airspace, the European Union has launched a set of new services called U-space that allow for the support of unmanned aerial vehicle (UAV) management and conflict prevention for flights in the airspace. These services are based on communication technology, which is expected to be the key enabler to unlock the underlying potential of UAV operations. In this regard, the 5G mobile network is envisioned to be the communication standard to support diverse UAV operations and applications. In this article, we propose a novel architecture that integrates 5G systems (5GSs) with U-space. The main aim consists of providing a reference design that demonstrates how 5G can support U-space services and shows the interactions among different stakeholders. Further, we introduce the 5G!Drones project, which relies on the proposed architecture to test UAV use-case scenarios on top of the 5G infrastructure.

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Oussama Bekkouche

Papers

Aerial Control System for Spectrum Efficiency in UAV-to-Cellular Communications

UAVs Traffic Control Based on Multi-Access Edge Computing

A Service-Based Architecture for Enabling UAV Enhanced Network Services

Toward a UTM-Based Service Orchestration for UAVs in MEC-NFV Environment

Supporting Unmanned Aerial Vehicle Services in 5G Networks: New High-Level Architecture Integrating 5G With U-Space