Unmanned Aerial Vehicle Applications over Cellular Networks for 5G and Beyond

Zhang, Hongliang; Song, Lingyang; Han, Zhu

doi:10.1007/978-3-030-33039-2

Cited by 63 publications

(47 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sundar and Rathinam [12] suggest a mathematical model and algorithm for UAVRP with the presence of refuelling depots. Zhang et al [13] discuss how to plan the time-variant placements of the UAVs served as base station (BS)/relay, and cover the applications of UAV in Assisted Cellular Communications and sensing purposes. Zhang et al [14] propose a cooperative sense-and-send protocol, in which a UAV can upload sensory data with the help of a UAV relay, to provide a better communication QoS for the sensing tasks.…”

Section: Related Workmentioning

confidence: 99%

An enhanced genetic algorithm for unmanned aerial vehicle logistics scheduling

Yuan

Zhu

et al. 2021

IET Communications

View full text Add to dashboard Cite

This paper examines a scheduling problem with heterogeneous logistics unmanned aerial vehicles (UAVs) in urban environment. Different from traditional vehicle routing problem (VRP), it introduces some new characteristics such as the loading capacity, the maximum flight time and the flight speed. As a variant of VRP, the considered scheduling problem is known to be an non-deterministic Polynomial (NP)-hard problem. The UAV scheduling problem model with the heterogeneous UAV settings is formulated first. Secondly, a genetic-based algorithm framework is presented for solving the scheduling problem, in which the encoding/decoding method, the initial population generation method and genetic operations are delicately designed. In order to reduce the search space and faster the execution of this algorithm, a weight-based loading method is adopted. For the purpose of performance evaluation and statistical analysis, the proposed algorithm is compared with the other two existing algorithms. The experimental results show that the presented algorithm can solve this problem efficiently.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Related Workmentioning

confidence: 99%

An enhanced genetic algorithm for unmanned aerial vehicle logistics scheduling

Yuan

Zhu

et al. 2021

IET Communications

View full text Add to dashboard Cite

show abstract

“…Since UAVs fly at high altitudes, the line-of-sight (LoS) components usually exist in the transmissions from UAVs to their mobile devices and the BS on the ground [28], [29]. Thus, the channel characteristic of the air-to-ground communications is different from that in traditional terrestrial communications.…”

Section: Uav Transmissionmentioning

confidence: 99%

Cellular UAV-to-Device Communications: Trajectory Design and Mode Selection by Multi-Agent Deep Reinforcement Learning

Zhang

et al. 2020

IEEE Trans. Commun.

Self Cite

View full text Add to dashboard Cite

In the current unmanned aircraft systems (UASs) for sensing services, unmanned aerial vehicles (UAVs) transmit their sensory data to terrestrial mobile devices over the unlicensed spectrum.However, the interference from surrounding terminals is uncontrollable due to the opportunistic channel access. In this paper, we consider a cellular Internet of UAVs to guarantee the Quality-of-Service (QoS), where the sensory data can be transmitted to the mobile devices either by UAV-to-Device (U2D) communications over cellular networks, or directly through the base station (BS). Since UAVs' sensing and transmission may influence their trajectories, we study the trajectory design problem for UAVs in consideration of their sensing and transmission. This is a Markov decision problem (MDP) with a large state-action space, and thus, we utilize multi-agent deep reinforcement learning (DRL) to approximate the state-action space, and then propose a multi-UAV trajectory design algorithm to solve this problem.Simulation results show that our proposed algorithm can achieve a higher total utility than policy gradient algorithm and single-agent algorithm. ). I. INTRODUCTIONWith high mobility and low operational cost, unmanned aerial vehicle (UAV) is recognized as a powerful facility to provide sensing services [2], [3], which has found use in a wide range of sensing applications including traffic monitoring [4], industrial inspection [5], precision agriculture [6], and fire surveillance [7]. In the current unmanned aircraft systems (UASs) for sensing services, UAVs transmit their sensory data to terrestrial mobile devices 1 over the unlicensed spectrum [8]. However, due to the opportunistic channel access at the media access control (MAC) layer, the interference from surrounding terminals is uncontrollable, and the Quality-of-Service (QoS) for sensing services cannot be guaranteed. Therefore, a more reliable network is necessary.As an effective solution, terrestrial cellular networks can be utilized to provide UAV sensing services with guaranteed QoS, which we refer to as the cellular Internet of UAVs [9], [10]. In the cellular Internet of UAVs, UAVs first transmit the sensory data to the BS, after which the BS sends the received data to mobile devices. When UAVs fly far from their sensing targets, they may suffer low sensing quality, while if UAVs are far from the BS, it is difficult to transmit their sensory data back to the BS. Therefore, to ensure the QoS, UAVs should jointly take sensing and transmission into consideration in the designing of their trajectories. However, when UAVs are close to their mobile devices, the data rate can be further improved if UAVs can directly transmit the sensory data to proximal mobile devices, namely the UAV-to-Device (U2D) communications, rather than transmit through cellular communications.In this paper, we propose to enable U2D communications in the cellular Internet of UAVs.We consider a orthogonal frequency division multiple access (OFDMA) cellular Internet of UAVs 2 , where the sensory data ca...

show abstract

“…As illustrated in Fig. 1 UAVs with onboard sensors are deployed in the cellular networks to provide various sensing services, which is called the cellular Internet of UAVs [7]. In the cellular Internet of UAVs, the sensory data is transmitted to the terrestrial user equipments (UEs) directly or to a remote sever through the base stations (BSs) according to different applications [8].…”

Section: Introductionmentioning

confidence: 99%

Beyond D2D: Full Dimension UAV-to-Everything Communications in 6G

Zhang

Song

2020

IEEE Trans. Veh. Technol.

Self Cite

120

View full text Add to dashboard Cite

In this paper, we consider an Internet of unmanned aerial vehicles (UAVs) over cellular networks, where UAVs work as aerial users to collect various sensory data, and send the collected data to their transmission destinations over cellular links. Unlike the terrestrial users in the conventional cellular networks, different UAVs have various communication requirements due to their sensing applications, and a more flexible communication framework is in demand. To tackle this problem, we propose a UAV-to-Everything (U2X) networking, which enables the UAVs to adjust their communication modes full dimensionally according to the requirements of their sensing applications. In this article, we first introduce the concept of U2X communications, and elaborate on its three communication modes. Afterwards, we discuss the key techniques of the U2X communications, including joint sensing and transmission protocol, UAV trajectory design, and radio resource management. A reinforcement learningbased mathematical framework for U2X communications is then proposed. Finally, the extensions of the U2X communications are presented.2) UAV Transmission: After UAV sensing, the collected data will be transmitted to the corresponding BS or UE, which is defined as the transmission destination. For each UAV, the transmission channel model and frequency band are regulated by the 3GPP [23]. To satisfy the transmission requirements raised by various applications, we provide three communication modes for the UAVs, namely U2N communications, U2U communications, and U2D communications. The communication mode for each link is selected by the UAV according to its location and the communication requirements of the sensing application. The descriptions of these three communication modes will be elaborated on in Section II-B.

show abstract

Unmanned Aerial Vehicle Applications over Cellular Networks for 5G and Beyond

Cited by 63 publications

References 0 publications

An enhanced genetic algorithm for unmanned aerial vehicle logistics scheduling

An enhanced genetic algorithm for unmanned aerial vehicle logistics scheduling

Cellular UAV-to-Device Communications: Trajectory Design and Mode Selection by Multi-Agent Deep Reinforcement Learning

Beyond D2D: Full Dimension UAV-to-Everything Communications in 6G

Contact Info

Product

Resources

About