The Future of Aerial Communications: A Survey of IRS-Enhanced UAV Communication Technologies
- URL: http://arxiv.org/abs/2407.01576v1
- Date: Sun, 2 Jun 2024 09:58:53 GMT
- Title: The Future of Aerial Communications: A Survey of IRS-Enhanced UAV Communication Technologies
- Authors: Zina Chkirbene, Ala Gouissem, Ridha Hamila, Devrim Unal,
- Abstract summary: The advent of Intelligent Reflecting Surfaces (IRS) and Unmanned Aerial Vehicles (UAVs) is setting a new benchmark in the field of wireless communications.
IRS, with their groundbreaking ability to manipulate electromagnetic waves, have opened avenues for substantial enhancements in signal quality, network efficiency, and spectral usage.
UAVs have emerged as dynamic, versatile elements within communication networks, offering high mobility and the ability to access and enhance coverage in areas where traditional, fixed infrastructure falls short.
- Score: 2.8002534443865987
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The advent of Intelligent Reflecting Surfaces (IRS) and Unmanned Aerial Vehicles (UAVs) is setting a new benchmark in the field of wireless communications. IRS, with their groundbreaking ability to manipulate electromagnetic waves, have opened avenues for substantial enhancements in signal quality, network efficiency, and spectral usage. These surfaces dynamically reconfigure the propagation environment, leading to optimized signal paths and reduced interference. Concurrently, UAVs have emerged as dynamic, versatile elements within communication networks, offering high mobility and the ability to access and enhance coverage in areas where traditional, fixed infrastructure falls short. This paper presents a comprehensive survey on the synergistic integration of IRS and UAVs in wireless networks, highlighting how this innovative combination substantially boosts network performance, particularly in terms of security, energy efficiency, and reliability. The versatility of UAVs, combined with the signal-manipulating prowess of IRS, creates a potent solution for overcoming the limitations of conventional communication setups, especially in challenging and underserved environments. Furthermore, the survey delves into the cutting-edge realm of Machine Learning (ML), exploring its role in the strategic deployment and operational optimization of UAVs equipped with IRS. The paper also underscores the latest research and practical advancements in this field, providing insights into real-world applications and experimental setups. It concludes by discussing the future prospects and potential directions for this emerging technology, positioning the IRS-UAV integration as a transformative force in the landscape of next-generation wireless
Related papers
- Deep Reinforcement Learning Based Placement for Integrated Access
Backhauling in UAV-Assisted Wireless Networks [6.895620511689995]
This paper introduces a novel approach leveraging deep reinforcement learning (DRL) to optimize UAV placement in real-time.
The unique contribution of this work lies in its ability to autonomously position UAVs in a way that not only ensures robust connectivity to ground users but also maintains seamless integration with central network infrastructure.
arXiv Detail & Related papers (2023-12-21T19:02:27Z) - UAV-aided RF Mapping for Sensing and Connectivity in Wireless Networks [52.14281905671453]
The use of unmanned aerial vehicles (UAV) as flying radio access network (RAN) nodes offers a promising complement to traditional fixed terrestrial deployments.
Radio mapping is one of the challenges related to this task, referred here as radio mapping.
The advantages induced by radio-mapping in terms of connectivity, sensing, and localization performance are illustrated.
arXiv Detail & Related papers (2022-05-06T16:16:08Z) - 5G Network on Wings: A Deep Reinforcement Learning Approach to the
UAV-based Integrated Access and Backhaul [11.197456628712846]
Unmanned aerial vehicle (UAV) based aerial networks offer a promising alternative for fast, flexible, and reliable wireless communications.
In this paper, we study how to control multiple UAV-BSs in both static and dynamic environments.
Deep reinforcement learning algorithm is developed to jointly optimize the three-dimensional placement of these multiple UAV-BSs.
arXiv Detail & Related papers (2022-02-04T07:45:06Z) - RIS-assisted UAV Communications for IoT with Wireless Power Transfer
Using Deep Reinforcement Learning [75.677197535939]
We propose a simultaneous wireless power transfer and information transmission scheme for IoT devices with support from unmanned aerial vehicle (UAV) communications.
In a first phase, IoT devices harvest energy from the UAV through wireless power transfer; and then in a second phase, the UAV collects data from the IoT devices through information transmission.
We formulate a Markov decision process and propose two deep reinforcement learning algorithms to solve the optimization problem of maximizing the total network sum-rate.
arXiv Detail & Related papers (2021-08-05T23:55:44Z) - A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From
Communications to Sensing and Intelligence [152.89360859658296]
5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC)
On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in 3D space.
On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference.
arXiv Detail & Related papers (2020-10-19T08:56:04Z) - Phase Configuration Learning in Wireless Networks with Multiple
Reconfigurable Intelligent Surfaces [50.622375361505824]
Reconfigurable Intelligent Surfaces (RISs) are highly scalable technology capable of offering dynamic control of electro-magnetic wave propagation.
One of the major challenges with RIS-empowered wireless communications is the low-overhead dynamic configuration of multiple RISs.
We devise low-complexity supervised learning approaches for the RISs' phase configurations.
arXiv Detail & Related papers (2020-10-09T05:35:27Z) - Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial [64.77665786141166]
Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal prorogation in wireless networks.
IRS is capable of dynamically altering wireless channels to enhance the communication performance.
Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks.
arXiv Detail & Related papers (2020-07-06T13:59:09Z) - Artificial Intelligence Aided Next-Generation Networks Relying on UAVs [140.42435857856455]
Artificial intelligence (AI) assisted unmanned aerial vehicle (UAV) aided next-generation networking is proposed for dynamic environments.
In the AI-enabled UAV-aided wireless networks (UAWN), multiple UAVs are employed as aerial base stations, which are capable of rapidly adapting to the dynamic environment.
As a benefit of the AI framework, several challenges of conventional UAWN may be circumvented, leading to enhanced network performance, improved reliability and agile adaptivity.
arXiv Detail & Related papers (2020-01-28T15:10:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.