AI-Powered Energy-Efficient UAV Communications

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Communications".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 1446

Special Issue Editors

Computational learning theory team, RIKEN-Advanced Intelligence Center, Fukuoka 819-0395 Japan
Interests: wireless communications; machine learning; online learning; 5G, B5G, and 6G systems; image processing; millimeter waves; RIS systems; the Internet of things
Special Issues, Collections and Topics in MDPI journals
Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
Interests: cybersecurity; artificial intelligence (AI); internet of things (IoT); smart grids; 5G/6G networks; vehicular networks; communication networks; image processing; signal processing; smart healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The efficient use of energy is critical for the successful deployment and operation of unmanned aerial vehicles (UAVs) in a variety of applications, including communication networks, surveillance, and transportation. The increasing demand for UAVs in different industries such as agriculture, logistics, and emergency response has led to the development of more advanced and sophisticated UAVs. However, the limited on-board energy resources of UAVs pose a significant challenge for their long-term operation and endurance. Additionally, machine learning and AI can enable UAVs to make more informed and intelligent decisions regarding their operations, leading to more energy-efficient and sustainable UAV deployment. Furthermore, the integration of recent technologies like reconfigurable intelligent surface (RIS), non-orthogonal multiple access (NOMA), satellites, etc. with UAVs empowers their application areas and future directions. In this Special Issue, we focus on recent developments and advances in energy-efficient models for UAV communications, including efficient UAV trajectory planning, energy-efficient UAV-NOMA and UAV-RIS systems, resource allocation, etc. The goal of this Special Issue is to provide a comprehensive overview of the current state-of-the-art in this field and to identify key areas for future research. The papers included in this Special Issue will cover a wide range of topics, including energy-efficient routing, energy-aware scheduling, and energy-efficient resource allocation. We believe that this Special Issue will be of interest to researchers, engineers, and practitioners working in the field of UAV communications and energy efficiency.

The papers included in this Special Issue will cover a wide range of topics, including but not limited to:

  • Energy-aware AI models for UAV communications;
  • Energy-efficient routing algorithms to optimize the trade-off between energy consumption and communication performance;
  • Energy-efficient UAV-NOMA systems;
  • Energy-efficient UAV-RIS communications;
  • Energy-aware scheduling and task allocation methods that consider the energy constraints of UAVs and the requirements of communication systems;
  • Energy-efficient resource allocation techniques that optimize the use of the radio-frequency spectrum and power allocation to reduce energy consumption;
  • Battery management and charging methods for UAVs that extend the UAVs’ flight time;
  • Studies on the energy consumption of different communication technologies such as cellular, satellite, and Wi-Fi, and how they can be applied to UAVs;
  • Development of energy harvesting techniques for UAVs that can recharge the UAVs’ batteries during flight;
  • Research on the environmental impact of UAVs in terms of energy consumption, and how to reduce their carbon footprint.

Dr. Sherief Hashima
Dr. Mostafa Fouda
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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

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

  • energy aware
  • Machine Learning
  • UAV-NOMA
  • UAV-RIS
  • UAV-Assisted Communications
  • B5G/6G

Published Papers (1 paper)

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Research

18 pages, 1298 KiB  
Article
Budgeted Bandits for Power Allocation and Trajectory Planning in UAV-NOMA Aided Networks
Drones 2023, 7(8), 518; https://doi.org/10.3390/drones7080518 - 07 Aug 2023
Cited by 2 | Viewed by 787
Abstract
On one hand combining Unmanned Aerial Vehicles (UAVs) and Non-Orthogonal Multiple Access (NOMA) is a remarkable direction to sustain the exponentially growing traffic requirements of the forthcoming Sixth Generation (6G) networks. In this paper, we investigate effective Power Allocation (PA) and Trajectory Planning [...] Read more.
On one hand combining Unmanned Aerial Vehicles (UAVs) and Non-Orthogonal Multiple Access (NOMA) is a remarkable direction to sustain the exponentially growing traffic requirements of the forthcoming Sixth Generation (6G) networks. In this paper, we investigate effective Power Allocation (PA) and Trajectory Planning Algorithm (TPA) for UAV-aided NOMA systems to assist multiple survivors in a post-disaster scenario, where ground stations are malfunctioned. Here, the UAV maneuvers to collect data from survivors, which are grouped in multiple clusters within the disaster area, to satisfy their traffic demands. On the other hand, while the problem is formulated as Budgeted Multi-Armed Bandits (BMABs) that optimize the UAV trajectory and minimize battery consumption, challenges may arise in real-world scenarios. Herein, the UAV is the bandit player, the disaster area clusters are the bandit arms, the sum rate of each cluster is the payoff, and the UAV energy consumption is the budget. Hence, to tackle these challenges, two Upper Confidence Bound (UCB) BMAB schemes are leveraged to handle this issue, namely BUCB1 and BUCB2. Simulation results confirm the superior performance of the proposed BMAB solution against benchmark solutions for UAV-aided NOMA communication. Notably, the BMAB-NOMA solution exhibits remarkable improvements, achieving 60% enhancement in the total number of assisted survivors, 80% improvement in convergence speed, and a considerable amount of energy saving compared to UAV-OMA. Full article
(This article belongs to the Special Issue AI-Powered Energy-Efficient UAV Communications)
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