A Near-Optimal UAV-Aided Radio Coverage Strategy for Dense Urban Areas

Li, Xiaowei; Yao, Haipeng; Wang, Jingjing; Xu, Xiaobin; Jiang, Chunxiao; Hanzo, Lajos

doi:10.1109/tvt.2019.2927425

Cited by 160 publications

(64 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Global throughput maximization [92]- [94] G2A/A2G throughput maximization [83], [95], [96] Maximizing energy efficiency [97] Maximizing energy efficiency [98], [99] Dynamic topology/swarm [100], [101] A2G/G2A localization [102], [103] Multi-UAV's resource allocation [104]- [106] Edge computing [107] Dense UAV networks [108] Ground node access [109], [110] station is designed with the following functions: wireless communication modules, computing/storage units with large enough capacity, intelligent decision algorithms for centralized mission planning, remote monitoring mechanisms, and recall mechanisms. The communication modules are crucial to achieve remote control for UAVs.…”

Section: Research Issuesmentioning

confidence: 99%

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Liu

Dai

Wang

et al. 2020

Computer Communications

180

View full text Add to dashboard Cite

The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole "things" aspect to the omnipotent role of "intelligent connection of things". Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: 1) scalability requiring a scalable network architecture with ubiquitous coverage, 2) intelligence requiring a global computing plane enabling intelligent things, 3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE.

show abstract

Section: Research Issuesmentioning

confidence: 99%

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Liu

Dai

Wang

et al. 2020

Computer Communications

180

View full text Add to dashboard Cite

show abstract

“…To further enhance performance, this operation can be jointly considered and addressed with appropriate communication protocols, such as scheduling and resource allocation [7], [43]. 4) SWAP constraints: Unlike gBSs, which benefit from stable power supplies or rechargeable batteries, SWAP constraints represent major challenges for UAVs in achieving sustainable and long-term operations.…”

Section: A Uav Characteristicsmentioning

confidence: 99%

Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting

Jaafar

Naser

Muhaidat

et al. 2020

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

Recently, interest on the utilization of unmanned aerial vehicles (UAVs) has aroused. Specifically, UAVs can be used in cellular networks as aerial users for delivery, surveillance, rescue search, or as an aerial base station (aBS) for communication with ground users in remote uncovered areas or in dense environments requiring prompt high capacity. Aiming to satisfy the high requirements of wireless aerial networks, several multiple access techniques have been investigated. In particular, space-division multiple access (SDMA) and power-domain non-orthogonal multiple access (NOMA) present promising multiplexing gains for aerial downlink and uplink. Nevertheless, these gains are limited as they depend on the conditions of the environment. Hence, a generalized scheme has been recently proposed, called rate-splitting multiple access (RSMA), which is capable of achieving better spectral efficiency gains compared to SDMA and NOMA. In this paper, we present a comprehensive survey of key multiple access technologies adopted for aerial networks, where aBSs are deployed to serve ground users. Since there have been only sporadic results reported on the use of RSMA in aerial systems, we aim to extend the discussion on this topic by modelling and analyzing the weighted sum-rate performance of a two-user network served by an RSMA-based aBS. Finally, related open issues and future research directions are exposed. Index Terms-Orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), rate-splitting multiple access (RSMA), unmanned aerial vehicle (UAV), survey. I. INTRODUCTION D UE to the unprecedented growth of mobile data traffic and stringent quality-of-service (QoS) requirements, recent research efforts have focused on several key enabling technologies for 5G networks and beyond [1], such as millimeter waves (mmWave), terahertz (THz) communications, multiple-input multiple-output (MIMO), massive MIMO, multiple access techniques, relaying, cognitive radio, and unmanned aerial networks [2]. In particular, unmanned aerial vehicles (UAVs) have demonstrated great potential in enabling new applications. For instance, UAVs can be used for aerial security inspection, traffic monitoring, smart agriculture, aerial delivery, etc. [3], [4] (and references therein). Furthermore, UAVs can be deployed as aerial base stations (aBSs) to provide wireless access to ground and aerial devices/users, in several scenarios such as temporary events, disasters when a terrestrial cellular network is not fully operational, and congestion due to unpredictable traffic surges, as well as aerial devices/users (e.g., cargo drones) [5], [6].

show abstract

“…separation, aggregation and aligment) [6]. Computational swarm intelligence mechanisms, like the PSO and genetic algorithms, have been investigated among others by [8]- [10] while [11], [28] have proposed the application of well-known robotic mobility models on FANETs. At the same time, few works have taken into account the Quality of Service (QoS) of wireless links between the ground nodes and the UAVs, that is a key requirement for applications with strict requirements (e.g.…”

Section: A Motivationsmentioning

confidence: 99%

“…In [8] and [21] the authors investigate the persistent coverage problem, i.e. how to increase the service time of an aerial mesh exploiting the presence of charging stations on the ground: the optimization problem is formulated by means of distributed PSO techniques in [8] and of game theory in [21]. UAV group mobility based on particle swarming is investigated also in [6], [9] and [10].…”

Section: Related Workmentioning

confidence: 99%

A GPS-Free Flocking Model for Aerial Mesh Deployments in Disaster-Recovery Scenarios

et al. 2020

View full text Add to dashboard Cite

In the aftermath of a large-scale emergency, Unmanned Aerial Vehicles (UAVs) can play a key role as mobile communication systems supporting rescue operations on the ground. At the same time, the deployment of autonomous UAV swarms still poses severe challenges in terms of distributed mobility, swarm connectivity and mesh networking. To this purpose, we propose ELAPSE (aErial LocAl Positioning System for Emergency), a novel, distributed framework for aerial mesh deployment that supports discovery and multi-hop connectivity among rescue personnel and emergency requesters. ELAPSE integrates components of swarm mobility, positioning and Quality-of-Service (QoS) support, while targeting UAV devices at different levels of hardware complexity. Three contributions are provided in this study. First, we present a novel, bio-inspired swarm mobility algorithm which natively addresses QoS-based aerial mesh connectivity, coverage of the ground nodes and UAV collision avoidance through the abstraction of virtual springs. Second, we investigates its implementation when geo-location capabilities are not available: to this aim, we propose local-based and cooperative-based techniques through which each UAV can estimate the position of its neighbours, and hence correctly adjust its direction and speed. Third, we analyze the feasibility of the ELAPSE framework through a twofold evaluation: i.e. a large-scale OMNeT++ simulation showing the effectiveness of the distributed mesh formation and localization techniques, and a small-case ground robotic testbed demonstrating the impact of QoS mechanisms on the system operations.

show abstract

A Near-Optimal UAV-Aided Radio Coverage Strategy for Dense Urban Areas

Cited by 160 publications

References 38 publications

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting

A GPS-Free Flocking Model for Aerial Mesh Deployments in Disaster-Recovery Scenarios

Contact Info

Product

Resources

About