sensors-logo

Journal Browser

Journal Browser

Wireless Communications with Unmanned Aerial Vehicles (UAV)

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

Deadline for manuscript submissions: 10 September 2024 | Viewed by 1101

Special Issue Editors


E-Mail Website
Guest Editor
Department of Control Science and Engineering, Tongji University, Shanghai 201804, China
Interests: distributed optimization and game; neural systems and autonomous agents

E-Mail Website
Guest Editor
Department of Control Science and Engineering, Tongji University, Shanghai 201804, China
Interests: 5G/6G; wireless power transfer; Internet of Things (loT); artificial intelligence(AI)

Special Issue Information

Dear Colleagues,

With their high mobility and low cost, unmanned aerial vehicles (UAVs), also commonly known as drones or remotely piloted aircrafts, have found a wide range of applications during the past few decades. The use of UAVs for achieving high-speed wireless communications is expected to play an important role in future communication systems. This is mainly because: 1) on-demand UAV systems are more cost-effective and can be much more swiftly deployed; 2) with the aid of low-altitude UAVs, short-range line-of-sight communication links can be established in most scenarios; and 3) the maneuverability of UAVs offers new opportunities for performance enhancement.

Despite the many promising benefits, wireless communications with UAVs are also faced with several new design challenges. For example: 1) additional control and non-payload communications links with much more stringent latency and security requirements are needed in UAV systems for supporting safety-critical functions; 2) the high-mobility environment of UAV systems generally results in highly dynamic network topologies; 3) the size, weight, and power constraints of UAVs could limit their communication, computation, and endurance capabilities; and 4) due to the mobility of UAVs as well as the lack of fixed backhaul links and centralized control, effective interference management techniques specifically designed for UAV-aided cellular coverage are needed. Thus, this area still deserves attention and research.

Prof. Dr. Peng Yi 
Prof. Dr. Qingwen Liu
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. Sensors is an international peer-reviewed open access semimonthly 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

  • unmanned aerial vehicles (UAVs)
  • wireless communications
  • high mobility
  • network topologies
  • interference management

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

25 pages, 4510 KiB  
Article
Multi-Objective Optimization in Air-to-Air Communication System Based on Multi-Agent Deep Reinforcement Learning
by Shaofu Lin, Yingying Chen and Shuopeng Li
Sensors 2023, 23(23), 9541; https://doi.org/10.3390/s23239541 - 30 Nov 2023
Viewed by 695
Abstract
With the advantages of real-time data processing and flexible deployment, unmanned aerial vehicle (UAV)-assisted mobile edge computing systems are widely used in both civil and military fields. However, due to limited energy, it is usually difficult for UAVs to stay in the air [...] Read more.
With the advantages of real-time data processing and flexible deployment, unmanned aerial vehicle (UAV)-assisted mobile edge computing systems are widely used in both civil and military fields. However, due to limited energy, it is usually difficult for UAVs to stay in the air for long periods and to perform computational tasks. In this paper, we propose a full-duplex air-to-air communication system (A2ACS) model combining mobile edge computing and wireless power transfer technologies, aiming to effectively reduce the computational latency and energy consumption of UAVs, while ensuring that the UAVs do not interrupt the mission or leave the work area due to insufficient energy. In this system, UAVs collect energy from external air-edge energy servers (AEESs) to power onboard batteries and offload computational tasks to AEESs to reduce latency. To optimize the system’s performance and balance the four objectives, including the system throughput, the number of low-power alarms of UAVs, the total energy received by UAVs and the energy consumption of AEESs, we develop a multi-objective optimization framework. Considering that AEESs require rapid decision-making in a dynamic environment, an algorithm based on multi-agent deep deterministic policy gradient (MADDPG) is proposed, to optimize the AEESs’ service location and to control the power of energy transfer. While training, the agents learn the optimal policy given the optimization weight conditions. Furthermore, we adopt the K-means algorithm to determine the association between AEESs and UAVs to ensure fairness. Simulated experiment results show that the proposed MODDPG (multi-objective DDPG) algorithm has better performance than the baseline algorithms, such as the genetic algorithm and other deep reinforcement learning algorithms. Full article
(This article belongs to the Special Issue Wireless Communications with Unmanned Aerial Vehicles (UAV))
Show Figures

Figure 1

Back to TopTop