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UAV Assisted 5G and Future Wireless Networks

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 9415

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Special Issue Editors

Prof. Dr. Chunbo Luo

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Guest Editor

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
Interests: unmanned aerial vehicles; wireless networks

Dr. Cheng Jin

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Guest Editor

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: millimeter and THz antennas and metasurface; THz radar sensors; wideband communication
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Prof. Dr. Gun Li

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Guest Editor

School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
Interests: unmanned aerial vehicles; control theory; navigation; signal processing

Prof. Dr. Junyu Lai

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Guest Editor

School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
Interests: network modeling, simulation & emulation; space-ground integrated networks; mobile ad-hoc networks

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) are becoming increasingly important for 5G and beyond (e.g., 6G) wireless networks by providing a game-changing technology to enhance real-world applications with ubiquitous, stable, and high-performance wireless connections. Despite their huge potential, UAV-assisted networks need to address key challenges such as stringent onboard resources, three-dimensional mobility, speed dynamicity, high link disruption, and large Doppler effects. Currently, extensive research on UAV and wireless networks has attracted significant efforts with fruitful research outcomes. This Special Issue aims to provide a focused platform for sharing state-of-the-art works on enabling technologies and novel applications of UAV assisted 5G and beyond wireless networks, with a particular focus on the following topics (but not limited to them):

Network architecture design;
UAV control strategies;
MIMO and beamforming;
Signal processing;
Hardware design, including VSLAM and acceleration algorithms;
Internet of Things;
Novel applications;
Multi-UAV systems;
Machine learning methods.

Prof. Dr. Chunbo Luo
Dr. Cheng Jin
Prof. Dr. Gun Li
Prof. Dr. Junyu Lai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

unmanned aerial vehicles
5G and beyond
millimeter wave
terahertz wave
learning-based signal processing
beamforming
aerial-ground channel modeling
space–air–ground integrated networks
integrated sensing and communication

Published Papers (4 papers)

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Research

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11 pages, 703 KiB

Open AccessCommunication

Optimization of the Trajectory, Transmit Power, and Power Splitting Ratio for Maximizing the Available Energy of a UAV-Aided SWIPT System

by Gitae Park and Kisong Lee

Sensors 2022, 22(23), 9081; https://doi.org/10.3390/s22239081 - 23 Nov 2022

Cited by 1 | Viewed by 864

Abstract

In this study, we investigate the maximization of the available energy for an unmanned aerial vehicle (UAV)-aided simultaneous wireless information and power transfer (SWIPT) system, in which the ground terminals (GTs) decode information and collect energy simultaneously from the downlink signal sent by the UAV based on a power splitting (PS) policy. To guarantee that each GT has a fair amount of available energy, our aim is to optimize the trajectory and transmit power of the UAV and the PS ratio of the GTs to maximize the minimum average available energy among all GTs while ensuring the average spectral efficiency requirement. To address the nonconvexity of the formulated optimization problem, we apply a successive convex optimization technique and propose an iterative algorithm to derive the optimal strategies of the UAV and GTs. Through performance evaluations, we show that the proposed scheme outperforms the existing baseline schemes in terms of the max–min available energy by adaptively controlling the optimization variables according to the situation. Full article

(This article belongs to the Special Issue UAV Assisted 5G and Future Wireless Networks)

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19 pages, 5363 KiB

Open AccessArticle

SAS-SEINet: A SNR-Aware Adaptive Scalable SEI Neural Network Accelerator Using Algorithm–Hardware Co-Design for High-Accuracy and Power-Efficient UAV Surveillance

by Jiayan Gan, Ang Hu, Ziyi Kang, Zhipeng Qu, Zhanxiang Yang, Rui Yang, Yibing Wang, Huaizong Shao and Jun Zhou

Sensors 2022, 22(17), 6532; https://doi.org/10.3390/s22176532 - 30 Aug 2022

Cited by 3 | Viewed by 1483

Abstract

As a potential air control measure, RF-based surveillance is one of the most commonly used unmanned aerial vehicles (UAV) surveillance methods that exploits specific emitter identification (SEI) technology to identify captured RF signal from ground controllers to UAVs. Recently many SEI algorithms based on deep convolution neural network (DCNN) have emerged. However, there is a lack of the implementation of specific hardware. This paper proposes a high-accuracy and power-efficient hardware accelerator using an algorithm–hardware co-design for UAV surveillance. For the algorithm, we propose a scalable SEI neural network with SNR-aware adaptive precision computation. With SNR awareness and precision reconfiguration, it can adaptively switch between DCNN and binary DCNN to cope with low SNR and high SNR tasks, respectively. In addition, a short-time Fourier transform (STFT) reusing DCNN method is proposed to pre-extract feature of UAV signal. For hardware, we designed a SNR sensing engine, denoising engine, and specialized DCNN engine with hybrid-precision convolution and memory access, aiming at SEI acceleration. Finally, we validate the effectiveness of our design on a FPGA, using a public UAV dataset. Compared with a state-of-the-art algorithm, our method can achieve the highest accuracy of 99.3% and an F1 score of 99.3%. Compared with other hardware designs, our accelerator can achieve the highest power efficiency of 40.12 Gops/W and 96.52 Gops/W with INT16 precision and binary precision. Full article

(This article belongs to the Special Issue UAV Assisted 5G and Future Wireless Networks)

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11 pages, 687 KiB

Open AccessArticle

RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication

by Xu Luo and Haifen Yang

Sensors 2022, 22(13), 4843; https://doi.org/10.3390/s22134843 - 27 Jun 2022

Cited by 1 | Viewed by 1417

Abstract

Unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) technology can simultaneously offer flexible communications and illumination to service ground users. Since a poor UAV working environment increases interference sent to the VLC link, there is a pressing need to further ensure reliable data communications. Run-length limited (RLL) codes are commonly utilized to ensure reliable data transmission and flicker-free perception in VLC technology. Conventional RLL decoding methods depend upon look-up tables, which can be prone to erroneous transmissions. This paper proposes a novel recurrent neural network (RNN)-based decoder for RLL codes that uses sequence to sequence (seq2seq) models. With a well-trained model, the decoder has a significant performance advantage over the look-up table method, and it can approach the bit error rate of maximum a posteriori (MAP) criterion-based decoding. Moreover, the decoder is use to deal with multiple frames simultaneously, such that the totality of RLL-coded frames can be decoded by only one-shot decoding within one time slot, which is able to enhance the system throughput. This shows our decoder’s great potential for practical UAV applications with VLC technology. Full article

(This article belongs to the Special Issue UAV Assisted 5G and Future Wireless Networks)

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Review

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36 pages, 2995 KiB

Open AccessReview

Handover Management for Drones in Future Mobile Networks—A Survey

by Ibraheem Shayea, Pabiola Dushi, Mohammed Banafaa, Rozeha A. Rashid, Sawsan Ali, Mohd Adib Sarijari, Yousef Ibrahim Daradkeh and Hafizal Mohamad

Sensors 2022, 22(17), 6424; https://doi.org/10.3390/s22176424 - 25 Aug 2022

Cited by 9 | Viewed by 4877

Abstract

Drones have attracted extensive attention for their environmental, civil, and military applications. Because of their low cost and flexibility in deployment, drones with communication capabilities are expected to play key important roles in Fifth Generation (5G), Sixth Generation (6G) mobile networks, and beyond. 6G and 5G are intended to be a full-coverage network capable of providing ubiquitous connections for space, air, ground, and underwater applications. Drones can provide airborne communication in a variety of cases, including as Aerial Base Stations (ABSs) for ground users, relays to link isolated nodes, and mobile users in wireless networks. However, variables such as the drone’s free-space propagation behavior at high altitudes and its exposure to antenna sidelobes can contribute to radio environment alterations. These differences may render existing mobility models and techniques as inefficient for connected drone applications. Therefore, drone connections may experience significant issues due to limited power, packet loss, high network congestion, and/or high movement speeds. More issues, such as frequent handovers, may emerge due to erroneous transmissions from limited coverage areas in drone networks. Therefore, the deployments of drones in future mobile networks, including 5G and 6G networks, will face a critical technical issue related to mobility and handover processes due to the main differences in drones’ characterizations. Therefore, drone networks require more efficient mobility and handover techniques to continuously maintain stable and reliable connection. More advanced mobility techniques and system reconfiguration are essential, in addition to an alternative framework to handle data transmission. This paper reviews numerous studies on handover management for connected drones in mobile communication networks. The work contributes to providing a more focused review of drone networks, mobility management for drones, and related works in the literature. The main challenges facing the implementation of connected drones are highlighted, especially those related to mobility management, in more detail. The analysis and discussion of this study indicates that, by adopting intelligent handover schemes that utilizing machine learning, deep learning, and automatic robust processes, the handover problems and related issues can be reduced significantly as compared to traditional techniques. Full article

(This article belongs to the Special Issue UAV Assisted 5G and Future Wireless Networks)

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