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Wireless Communications with Unmanned Aerial Vehicles (UAV)
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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: closed (10 September 2024) | Viewed by 4638

Special Issue Editors

Prof. Dr. Peng Yi

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
Prof. Dr. Qingwen Liu

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

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Keywords

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

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Published Papers (3 papers)

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Research

27 pages, 19890 KiB  
Article
The Possibility of Improving the Range and Quality of Air-to-Ground WebRTC-Based IoT Communication in Emergency Situations by Employing an External Transceiver
by Krzysztof Wajda, Agnieszka Chodorek and Robert Ryszard Chodorek
Sensors 2024, 24(20), 6533; https://doi.org/10.3390/s24206533 - 10 Oct 2024
Viewed by 1049
Abstract
The proliferation of new services, either interpersonal or machine-oriented, has generated new demands concerning the flexibility and efficiency of transmission. The ubiquity of multimedia communication in the current internet is seamlessly and successfully supported by the WebRTC concept. This paper reports on the [...] Read more.
The proliferation of new services, either interpersonal or machine-oriented, has generated new demands concerning the flexibility and efficiency of transmission. The ubiquity of multimedia communication in the current internet is seamlessly and successfully supported by the WebRTC concept. This paper reports on the study of the usage of a solution employing a proxy transmission unit for air-to-ground delivery of video streaming multiplexed with sensor data in the UAV-IoT system when using the WebRTC protocol stack. The comparative experiments were carried out for two cases employing the 802.11ac network with WebRTC: the first scenario (S1) without an external transceiver and the second scenario (S2) with an external transceiver working as a proxy of the ground receiver. The presented results compare the transmission conditions without (scenario S1) and with (scenario S2) the external transceiver in terms of the RSSI, the available data rate, and total throughput of transmission of multimedia data (video stream from the UAV camera and bursty data coming from employed sensors. The usefulness of the external transceiver used in a wide range of transmission conditions is clearly proven. Full article
(This article belongs to the Special Issue Wireless Communications with Unmanned Aerial Vehicles (UAV))
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14 pages, 645 KiB  
Article
Downlink Transmissions of UAV-RIS-Assisted Cell-Free Massive MIMO Systems: Location and Trajectory Optimization
by Qi Zhang , Jie Zhao , Rongcheng Zhang  and Longxiang Yang 
Sensors 2024, 24(13), 4064; https://doi.org/10.3390/s24134064 - 22 Jun 2024
Cited by 1 | Viewed by 1252
Abstract
In this paper, we investigate a cell-free massive multiple-input multiple-output (CF-mMIMO) system with a reconfigurable intelligent surface (RIS) carried by an unmanned aerial vehicle (UAV), called the UAV-RIS. Compared with the RIS located on the ground, the UAV-RIS has a wider coverage that [...] Read more.
In this paper, we investigate a cell-free massive multiple-input multiple-output (CF-mMIMO) system with a reconfigurable intelligent surface (RIS) carried by an unmanned aerial vehicle (UAV), called the UAV-RIS. Compared with the RIS located on the ground, the UAV-RIS has a wider coverage that can reflect all signals from access points (APs) and user equipment (UE). By correlating the UAV location with the Rician K-factor, we derive a closed-form approximation of the UE achievable downlink rate. Based on this, we obtain the optimal UAV location and RIS phase shift that can maximize the UE sum rate through an alternating optimization method. Simulation results have verified the accuracy of the derived approximation and shown that the UE sum rate can be significantly improved with the obtained optimal UAV location and RIS phase shift. Moreover, we find that with a uniform UE distribution, the UAV-RIS should fly to the center of the system, while with an uneven UE distribution, the UAV-RIS should fly above the area where UEs are gathered. In addition, we also design the best trajectory for the UAV-RIS to fly from its initial location to the optimal destination while maintaining the maximum UE sum rate per time slot during the flight. Full article
(This article belongs to the Special Issue Wireless Communications with Unmanned Aerial Vehicles (UAV))
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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
Cited by 1 | Viewed by 1588
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))
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