A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking

Xiao, Zhenyu; Zhu, Lipeng; Liu, Yanming; Yi, Pengfei; Zhang, Rui; Xia, Xiang-Gen; Schober, Robert

doi:10.1109/comst.2021.3124512

Cited by 233 publications

(94 citation statements)

References 337 publications

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“…It can also allow wide-area Internet-of-Things (IoT) access in areas not covered by terrestrial networks, ensuring emergency communications, agriculture monitoring, as well as collection of information on marine buoys, etc. With their inherent properties such as flexibility, mobility, and adaptive altitude, unmanned aerial vehicles (UAVs) have been considered as an indispensable component in future integrated terrestrial and aerial 6G networks to efficiently support massive access for wide-area IoT [4], [5]. Due to the low-cost and low-energy consumption features of typical IoT devices, as well as the complicated propagation condition of UAV-ground channels, disruptive techniques are needed to efficiently enable UAV-enabled wide-area massive IoT access [6]- [10].…”

Section: Introductionmentioning

confidence: 99%

“…Note that there are two main types of UAVs widely used in practice, i.e., the rotary-wing UAVs and the fixed-wing UAVs[4]. The rotary-wing UAV typically has a relatively low load capacity and limited space, which can restrict the use of massive antenna arrays[4],[5]. Hence, we consider smallscale MIMO at the rotary-wing UAV.…”

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confidence: 99%

“…Hence, we consider smallscale MIMO at the rotary-wing UAV. While, with a greater payload capacity and larger space[4],[5], a fixed-wing UAV can be equipped with LS-MIMO 3. The proposed scheme can be applied to efficiently collect the information of marine buoys or other maritime sensors.…”

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confidence: 99%

“…The proposed scheme can be applied to efficiently collect the information of marine buoys or other maritime sensors. The channel conditions in maritime communications are different from those in terrestrial communications due to the special environment over the sea surface[5],[9],[10]. This study is left to a future research work.…”

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confidence: 99%

“…It has been proved that signature sequences based on the Bernoulli distribution provide better performance than those based on the Gaussian distribution. Also, the probability that two devices have identical signature sequences can be neglected in massive IoT access[25] 5. In this section, we assume that the rotary-wing UAV hovers in the air when it collects data from IoT devices[4].…”

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confidence: 99%

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Joint Activity and Blind Information Detection for UAV-Assisted Massive IoT Access

Li¹,

Zhang²,

Gao³

et al. 2021

Preprint

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Grant-free non-coherent index-modulation (NC-IM) has been recently considered as an efficient massive access scheme for enabling cost-and energy-limited Internet-of-Things (IoT) devices that transmit small data packets. This paper investigates the grant-free NC-IM scheme combined with orthogonal frequency division multiplexing for applicant to unmanned aerial vehicle (UAV)-based massive IoT access. Specifically, each device is assigned a unique non-orthogonal signature sequence codebook. Each active device transmits one of its signature sequences in the given time-frequency resources, by modulating the information in the index of the transmitted signature sequence. For small-scale multiple-input multiple-output (MIMO) deployed at the UAV-based aerial base station (BS), by jointly exploiting the space-time-frequency domain device activity, we propose a computationally efficient space-time-frequency joint activity and blind information detection (JABID) algorithm with significantly improved detection performance. Furthermore, for large-scale MIMO deployed at the aerial BS, by leveraging the sparsity of the virtual angular-domain channels, we propose an angular-domain based JABID algorithm for improving the system performance with reduced access latency. In addition, for the case of high mobility IoT devices and/or UAVs, we introduce a timefrequency spread transmission (TFST) strategy for the proposed JABID algorithms to combat doubly-selective fading channels. Finally, extensive simulation results are illustrated to verify the superiority of the proposed algorithms and the TFST strategy over known state-of-the-art algorithms.

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Section: Introductionmentioning

confidence: 99%

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Joint Activity and Blind Information Detection for UAV-Assisted Massive IoT Access

Li¹,

Zhang²,

Gao³

et al. 2021

Preprint

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A three‐dimensional wideband mmWave multiple‐input multiple‐output channel model for aerial reconfigurable intelligent surface‐assisted communication systems

Wu,

Bian,

Wang

et al. 2023

Int J Communication

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SummaryThis paper presents a wideband millimeter wave (mmWave) channel model for aerial reconfigurable intelligent surface (ARIS) communications, in which the reconfigurable intelligent surface (RIS) is attached to an unmanned aerial vehicle (UAV) to enable intelligent signal reflections in the air. The model is established according to standard 3GPP channel models and considers important propagation factors, for example, large‐scale fading, small‐scale fading, and radiation pattern of reflection units (RUs). In addition, the model assumes three‐dimensional (3D) rotations of RIS and can capture its effects on channel characteristics. Based on the model, we propose a design method for RIS reflection phases. Statistics including channel amplitude, spatial, temporal, and frequency correlation functions (CFs), and space‐Doppler power spectral density (SD‐PSD) are obtained. The impacts of RU number, UAV height and speed, and RIS rotation on channel statistics are investigated. Results suggest that the designed reflection phases can effectively mitigate multipath fading and Doppler effect. Compared to the yaw angle of the UAV platform, channel characteristics are more sensitive to the roll and pitch angles.

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5G network slicing with unmanned aerial vehicles: Taxonomy, survey, and future directions

Bouzid

Chaib

Bensaad

et al. 2022

Trans Emerging Tel Tech

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With the ever‐expanding rise of network demands and user expectations, the fifth generation (5G) of cellular networks was envisioned to support a plethora of use cases and conflicting user demands. Next to providing traditional connectivity like its previous generations, 5G also promises to be a heterogeneous network connecting humans, vehicles, unmanned aerial vehicles (UAVs), smart devices, and more. These challenging expectations proved to be overwhelming for traditional network infrastructures to handle. Network slicing has emerged as a promising solution that can achieve such diverse, taxing, and sometimes conflicting requirements in a dynamic and programmable way. There is no denying that UAVs have attained significant focus and research in recent years, and with 5G already being deployed, UAVs can now exploit the capabilities of the new networks. Extensive research is being taken to integrate UAVs into networks, assisting and improving aspects like latency, coverage, and capacity. Motivated by these facts, this survey distinguishes itself from other works by jointly exploring 5G, network slicing, and UAVs. The main contributions of this article are to showcase how UAVs can assist networks, provide a taxonomy of UAVs in the context of network slicing, and survey works that contribute to network slicing with UAVs. In this article, we present a comprehensive survey on UAVs in the context of network slicing, covering contributions, and state‐of‐the‐art literature. We discuss network slicing in‐depth, focusing especially on the three major slices: enhanced Mobile BroadBand, massive machine type communications, and ultra‐reliable low‐latency communications. We provide an overview of 5G enablers, including software‐defined networking and network function virtualization. We cover UAVs and identify their roles in networks as both users and assistants. Furthermore, this survey provides insight into open issues and future research directions related to network slicing and UAVs before concluding.

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A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking

Cited by 233 publications

References 337 publications

Joint Activity and Blind Information Detection for UAV-Assisted Massive IoT Access

Joint Activity and Blind Information Detection for UAV-Assisted Massive IoT Access

A three‐dimensional wideband mmWave multiple‐input multiple‐output channel model for aerial reconfigurable intelligent surface‐assisted communication systems

5G network slicing with unmanned aerial vehicles: Taxonomy, survey, and future directions

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