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UAV Control and Communications in 5G and beyond 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: closed (10 June 2022) | Viewed by 13806
Please feel free to contact Guest Editors or Special Issue Editor ([email protected]) for any queries.

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Dr. George C. Alexandropoulos

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Guest Editor

Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
Interests: full-duplex systems; internet of things; (massive) millimeter-wave and THz systems; MIMO; physical layer security; reconfigurable intelligent surfaces; signal processing for communication; wireless transceiver architectures
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Dr. Evangelos Vlachos

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ATHENA Research Center - Industrial Systems Institute, 26504 Patra, Greece
Interests: wireless communications; cyber physical systems; adaptive control and optimisation; millimeter wave communication

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) have evolved considerably toward real-world applications, and they are expected to be an important component of the next-generation cellular networks that can facilitate high rate and low latency transmissions. With the advent of fifth-generation (5G) cellular networks and the significant increase of the number of UAVs, the opportunity for UAVs to participate in the realization of 5G opportunistic networks by carrying 5G base stations (BSs) to underserved areas has appeared, utilizing millimeter wave frequencies, hybrid analog/digital architectures, and intelligent surfaces. This allows the assistance or the replacement of ground BSs and the provision of bandwidth demanding services, such as ultrahigh definition (UHD) video streaming and augmented/extended reality, as well as other real-time and multimedia services. UAVs need to have the abilities of sensing and perceiving the environment, analyzing the sensed information, communicating, planning, and decision making, as well as acting using control algorithms and actuators. Communications have an incredibly significant role in the control and autonomous behavior of the UAV and swarm of UAVs. However, 5G and beyond wireless networks with UAVs are significantly different from conventional communication systems. This is due to the high altitude and high mobility of UAVs, the unique channel characteristics of UAV-to-ground and ground-to-UAV links, the asymmetric quality of service (QoS) requirements for command and control and mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the additional design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

This Special Issue will focus on key theoretical and practical design issues for control and communication aspects of 5G and beyond enabled UAVs. Topics of interest in this Special Issue include but are not limited to the following:

Channel modeling for UAV–ground and UAV–UAV communications;
New architectures and communication protocols for cellular-connected UAVs;
Energy-efficient transceiver hardware architecture for UAVs;
Interference mitigation for cellular-connected UAVs;
Massive MIMO/millimeter wave communications for cellular-connected UAVs;
3D aerial base station placement;
UAVs and reconfigurable intelligent surfaces;
Online/offline and machine-learning-based UAV trajectory optimization;
Joint trajectory design and resource allocation for UAV-assisted wireless communication;
Fundamental tradeoffs in UAV wireless networks;
Energy-efficient UAV communications;
UAV swarm in 5G and beyond;
UAV channel estimation and pilot decontamination;
Beam alignment and tracking for UAV high-frequency communications;
Physical layer security and techniques in wireless networks with UAVs;
Autonomous energy-efficient mission planning optimization;
Flight control system offloading utilizing multi-access edge computing.

Dr. George C. Alexandropoulos
Dr. Evangelos Vlachos
Guest Editors

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Keywords

unmanned aerial vehicles
UAV
massive MIMO
millimeter wave
energy efficiency
5G and beyond
UAV-assisted communications

Published Papers (5 papers)

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Research

25 pages, 759 KiB

Open AccessArticle

Joint Clustering and Resource Allocation Optimization in Ultra-Dense Networks with Multiple Drones as Small Cells Using Game Theory

by Tinh T. Bui, Long D. Nguyen, Ha Hoang Kha, Nguyen-Son Vo and Trung Q. Duong

Sensors 2023, 23(8), 3899; https://doi.org/10.3390/s23083899 - 11 Apr 2023

Cited by 1 | Viewed by 1910

Abstract

In this study, we consider the combination of clustering and resource allocation based on game theory in ultra-dense networks that consist of multiple macrocells using massive multiple-input multiple-output and a vast number of randomly distributed drones serving as small-cell base stations. In particular, to mitigate the intercell interference, we propose a coalition game for clustering small cells, with the utility function being the ratio of signal to interference. Then, the optimization problem of resource allocation is divided into two subproblems: subchannel allocation and power allocation. We use the Hungarian method, which is efficient for solving binary optimization problems, to assign the subchannels to users in each cluster of small cells. Additionally, a centralized algorithm with low computational complexity and a distributed algorithm based on the Stackelberg game are provided to maximize the network energy efficiency (EE). The numerical results demonstrate that the game-based method outperforms the centralized method in terms of execution time in small cells and is better than traditional clustering in terms of EE. Full article

(This article belongs to the Special Issue UAV Control and Communications in 5G and beyond Networks)

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21 pages, 8880 KiB

Open AccessArticle

Reliable Aerial Mobile Communications with RSRP & RSRQ Prediction Models for the Internet of Drones: A Machine Learning Approach

by Mehran Behjati, Muhammad Aidiel Zulkifley, Haider A. H. Alobaidy, Rosdiadee Nordin and Nor Fadzilah Abdullah

Sensors 2022, 22(15), 5522; https://doi.org/10.3390/s22155522 - 24 Jul 2022

Cited by 8 | Viewed by 2837

Abstract

The unmanned aerial vehicle (UAV) industry is moving toward beyond visual line of sight (BVLOS) operations to unlock future internet of drones applications, including unmanned environmental monitoring and long-range delivery services. A reliable and ubiquitous mobile communication link plays a vital role in ensuring flight safety. Cellular networks are considered one of the main enablers of BVLOS operations. However, the existing cellular networks are designed and optimized for terrestrial use cases. To investigate the reliability of provided aerial coverage by the terrestrial cellular base stations (BSs), this article proposes six machine learning-based models to predict reference signal received power (RSRP) and reference signal received quality (RSRQ) based on the multiple linear regression, polynomial, and logarithmic methods. In this regard, first, a UAV-to-BS measurement campaign was conducted in a 4G LTE network within a suburban environment. Then, the aerial coverage was statistically analyzed and the prediction methods were developed as a function of distance and elevation angle. The results reveal the capability of terrestrial BSs in providing aerial coverage under some circumstances, which mainly depends on the distance between the UAV and BS and flight height. The performance evaluation shows that the proposed RSRP and RSRQ models achieved RMSE of 4.37 dBm and 2.71 dB for testing samples, respectively. Full article

(This article belongs to the Special Issue UAV Control and Communications in 5G and beyond Networks)

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22 pages, 5875 KiB

Open AccessArticle

Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone

by Faris A. Almalki and Ben Othman Soufiene

Sensors 2022, 22(5), 1786; https://doi.org/10.3390/s22051786 - 24 Feb 2022

Cited by 12 | Viewed by 2517

Abstract

The coupling of drones and IoT is a major topics in academia and industry since it significantly contributes towards making human life safer and smarter. Using drones is seen as a robust approach for mobile remote sensing operations, such as search-and-rescue missions, due to their speed and efficiency, which could seriously affect victims’ chances of survival. This paper aims to modify the Hata-Davidson empirical propagation model based on RF drone measurement to conduct searches for missing persons in complex environments with rugged areas after manmade or natural disasters. A drone was coupled with a thermal FLIR lepton camera, a microcontroller, GPS, and weather station sensors. The proposed modified model utilized the least squares tuning algorithm to fit the data measured from the drone communication system. This enhanced the RF connectivity between the drone and the local authority, as well as leading to increased coverage footprint and, thus, the performance of wider search-and-rescue operations in a timely fashion using strip search patterns. The development of the proposed model considered both software simulation and hardware implementations. Since empirical propagation models are the most adjustable models, this study concludes with a comparison between the modified Hata-Davidson algorithm against other well-known modified empirical models for validation using root mean square error (RMSE). The experimental results show that the modified Hata-Davidson model outperforms the other empirical models, which in turn helps to identify missing persons and their locations using thermal imaging and a GPS sensor. Full article

(This article belongs to the Special Issue UAV Control and Communications in 5G and beyond Networks)

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19 pages, 24465 KiB

Open AccessArticle

Power Allocation and Energy Cooperation for UAV-Enabled MmWave Networks: A Multi-Agent Deep Reinforcement Learning Approach

by Mari Carmen Domingo

Sensors 2022, 22(1), 270; https://doi.org/10.3390/s22010270 - 30 Dec 2021

Cited by 5 | Viewed by 2016

Abstract

Unmanned Aerial Vehicle (UAV)-assisted cellular networks over the millimeter-wave (mmWave) frequency band can meet the requirements of a high data rate and flexible coverage in next-generation communication networks. However, higher propagation loss and the use of a large number of antennas in mmWave networks give rise to high energy consumption and UAVs are constrained by their low-capacity onboard battery. Energy harvesting (EH) is a viable solution to reduce the energy cost of UAV-enabled mmWave networks. However, the random nature of renewable energy makes it challenging to maintain robust connectivity in UAV-assisted terrestrial cellular networks. Energy cooperation allows UAVs to send their excessive energy to other UAVs with reduced energy. In this paper, we propose a power allocation algorithm based on energy harvesting and energy cooperation to maximize the throughput of a UAV-assisted mmWave cellular network. Since there is channel-state uncertainty and the amount of harvested energy can be treated as a stochastic process, we propose an optimal multi-agent deep reinforcement learning algorithm (DRL) named Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to solve the renewable energy resource allocation problem for throughput maximization. The simulation results show that the proposed algorithm outperforms the Random Power (RP), Maximal Power (MP) and value-based Deep Q-Learning (DQL) algorithms in terms of network throughput. Full article

(This article belongs to the Special Issue UAV Control and Communications in 5G and beyond Networks)

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13 pages, 1997 KiB

Open AccessCommunication

Deep Reinforcement Learning for UAV Trajectory Design Considering Mobile Ground Users

by Wonseok Lee, Young Jeon, Taejoon Kim and Young-Il Kim

Sensors 2021, 21(24), 8239; https://doi.org/10.3390/s21248239 - 9 Dec 2021

Cited by 12 | Viewed by 2795

Abstract

A network composed of unmanned aerial vehicles (UAVs), serving as base stations (UAV-BS network), is emerging as a promising component in next-generation communication systems. In the UAV-BS network, the optimal positioning of a UAV-BS is an essential requirement to establish line-of-sight (LoS) links for ground users. A novel deep Q-network (DQN)-based learning model enabling the optimal deployment of a UAV-BS is proposed. Moreover, without re-learning of the model and the acquisition of the path information of ground users, the proposed model presents the optimal UAV-BS trajectory while ground users move. Specifically, the proposed model optimizes the trajectory of a UAV-BS by maximizing the mean opinion score (MOS) for ground users who move to various paths. Furthermore, the proposed model is highly practical because, instead of the locations of individual mobile users, an average channel power gain is used as an input parameter. The accuracy of the proposed model is validated by comparing the results of the model with those of a mathematical optimization solver. Full article

(This article belongs to the Special Issue UAV Control and Communications in 5G and beyond Networks)

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