Cooperation Techniques for a Cellular Internet of Unmanned Aerial Vehicles

Zhang, Hongliang; Song, Lingyang; Han, Zhu; Poor, H. Vincent

doi:10.1109/mwc.2019.1800591

Cited by 70 publications

(41 citation statements)

References 14 publications

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“…The BS can then transmit the data to the destination nodes in the backbone network, and thereby providing high reliable data transmissions. For U2U communications, two UAVs in proximity can set up a direct link bypassing the BS, which enables the UAVs to perform cooperative sensing and transmission [15]. The data rates for U2U communications can be further improved by exploiting proximity gain and underlay nature.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Song

2020

IEEE Trans. Veh. Technol.

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116

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zhang

Song

2020

IEEE Trans. Veh. Technol.

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116

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“…The combination of unmanned aerial vehicles (UAV) and Internet services is already proposed as the concept of Internet of UAV (IoUAV) [ 16 , 17 , 18 ]. The Internet and drones in specific are also related in [ 19 , 20 ] as Internet of Drones (IoD).…”

Section: Introductionmentioning

confidence: 99%

Distributed Architecture for Unmanned Vehicle Services

Ramos

Ribeiro

Safadinho

et al. 2021

Sensors

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The demand for online services is increasing. Services that would require a long time to understand, use and master are becoming as transparent as possible to the users, that tend to focus only on the final goals. Combined with the advantages of the unmanned vehicles (UV), from the unmanned factor to the reduced size and costs, we found an opportunity to bring to users a wide variety of services supported by UV, through the Internet of Unmanned Vehicles (IoUV). Current solutions were analyzed and we discussed scalability and genericity as the principal concerns. Then, we proposed a solution that combines several services and UVs, available from anywhere at any time, from a cloud platform. The solution considers a cloud distributed architecture, composed by users, services, vehicles and a platform, interconnected through the Internet. Each vehicle provides to the platform an abstract and generic interface for the essential commands. Therefore, this modular design makes easier the creation of new services and the reuse of the different vehicles. To confirm the feasibility of the solution we implemented a prototype considering a cloud-hosted platform and the integration of custom-built small-sized cars, a custom-built quadcopter, and a commercial Vertical Take-Off and Landing (VTOL) aircraft. To validate the prototype and the vehicles’ remote control, we created several services accessible via a web browser and controlled through a computer keyboard. We tested the solution in a local network, remote networks and mobile networks (i.e., 3G and Long-Term Evolution (LTE)) and proved the benefits of decentralizing the communications into multiple point-to-point links for the remote control. Consequently, the solution can provide scalable UV-based services, with low technical effort, for anyone at anytime and anywhere.

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“…1. To realize the above visions, it is envisaged and studied by the Third Generation Partnership Project (3GPP) that the cellular networks are necessary for UAVs to execute sensing tasks, which we refer to as the cellular Internet of UAVs [3].…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we introduce the reinforcement learning approaches and their applications in 3 the cellular Internet of UAVs. Since reinforcement learning can enable UAVs to improve their policies for their objectives without a priori knowledge of the environment, it is suitable to address the key problems in the cellular Internet of UAVs [6], such as trajectory control and resource management.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for a Cellular Internet of UAVs: Protocol Design, Trajectory Control, and Resource Management

Zhang

Song

et al. 2020

IEEE Wireless Commun.

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Unmanned aerial vehicles (UAVs) can be powerful Internet-of-Things components to execute sensing tasks over the next-generation cellular networks, which are generally referred to as the cellular Internet of UAVs. However, due to the high mobility of UAVs and the shadowing in the air-to-ground channels, UAVs operate in an environment with dynamics and uncertainties. Therefore, UAVs need to improve the quality of service (QoS) of sensing and communication without complete information, which makes reinforcement learning suitable to be employed in the cellular Internet of UAVs. In this article, we propose a distributed sense-and-send protocol to coordinate the UAVs for sensing and transmission.Then, we apply reinforcement learning in the cellular Internet of UAVs to solve key problems such as trajectory control and resource management. Finally, we point out several potential future research directions.

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Cooperation Techniques for a Cellular Internet of Unmanned Aerial Vehicles

Cited by 70 publications

References 14 publications

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

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

Distributed Architecture for Unmanned Vehicle Services

Reinforcement Learning for a Cellular Internet of UAVs: Protocol Design, Trajectory Control, and Resource Management

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