Federated Learning Meets Intelligence Reflection Surface in Drones for Enabling 6G Networks: Challenges and Opportunities
The combination of drones and Intelligent Reflecting Surfaces (IRS) have emerged as potential technologies for improving the performance of six Generation (6G) communication networks by proactively modifying wireless communication through smart signal reflection and manoeuvre control. By deploying t...
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creator | Shvetsov, Alexey V. Alsamhi, Saeed Hamood Hawbani, Ammar Kumar, Santosh Srivastava, Sumit Agarwal, Sweta Rajput, Navin Singh Alammari, Amr A. Nashwan, Farhan M. A. |
description | The combination of drones and Intelligent Reflecting Surfaces (IRS) have emerged as potential technologies for improving the performance of six Generation (6G) communication networks by proactively modifying wireless communication through smart signal reflection and manoeuvre control. By deploying the IRS on drones, it becomes possible to improve the coverage and reliability of the communication network while reducing energy consumption and costs. Furthermore, integrating IRS with Federated Learning (FL) can further boost the performance of the drone network by enabling collaborative learning among multiple drones, leading to better and more efficient decision-making and holding great promise for enabling 6G communication networks. Therefore, we present a novel framework for FL meets IRS in drones for enabling 6G. In this framework, multiple IRS-equipped drone swarm are deployed to form a distributed wireless network, where FL techniques are used to collaborate with the learning process and optimize the reflection coefficients of each drone-IRS. This allows drone swarm to adapt to changing communication environments and improve the coverage and quality of wireless communication services. Integrating FL and IRS into drones offers several advantages over traditional wireless communication networks, including rapid deployment in emergencies or disasters, improved coverage and quality of communication services, and increased accessibility to remote areas. Finally, we highlight the challenges and opportunities of integrating FL and IRS into drones for researchers interested in drone networks. We also help drive innovation in developing 6G communication networks. |
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Therefore, we present a novel framework for FL meets IRS in drones for enabling 6G. In this framework, multiple IRS-equipped drone swarm are deployed to form a distributed wireless network, where FL techniques are used to collaborate with the learning process and optimize the reflection coefficients of each drone-IRS. This allows drone swarm to adapt to changing communication environments and improve the coverage and quality of wireless communication services. Integrating FL and IRS into drones offers several advantages over traditional wireless communication networks, including rapid deployment in emergencies or disasters, improved coverage and quality of communication services, and increased accessibility to remote areas. Finally, we highlight the challenges and opportunities of integrating FL and IRS into drones for researchers interested in drone networks. We also help drive innovation in developing 6G communication networks.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2023.3323399</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>6G mobile communication ; Array signal processing ; Collaboration ; Communication networks ; Communications networks ; drone swarm ; Drones ; Energy consumption ; Energy costs ; Federated learning ; IoT ; IRS ; Particle swarm optimization ; Quality of service ; Reflection ; Signal reflection ; Smart devices ; smart environment ; Wireless communication ; Wireless communications ; Wireless networks</subject><ispartof>IEEE access, 2023, Vol.11, p.130860-130887</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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A.</creatorcontrib><title>Federated Learning Meets Intelligence Reflection Surface in Drones for Enabling 6G Networks: Challenges and Opportunities</title><title>IEEE access</title><addtitle>Access</addtitle><description>The combination of drones and Intelligent Reflecting Surfaces (IRS) have emerged as potential technologies for improving the performance of six Generation (6G) communication networks by proactively modifying wireless communication through smart signal reflection and manoeuvre control. By deploying the IRS on drones, it becomes possible to improve the coverage and reliability of the communication network while reducing energy consumption and costs. Furthermore, integrating IRS with Federated Learning (FL) can further boost the performance of the drone network by enabling collaborative learning among multiple drones, leading to better and more efficient decision-making and holding great promise for enabling 6G communication networks. Therefore, we present a novel framework for FL meets IRS in drones for enabling 6G. In this framework, multiple IRS-equipped drone swarm are deployed to form a distributed wireless network, where FL techniques are used to collaborate with the learning process and optimize the reflection coefficients of each drone-IRS. This allows drone swarm to adapt to changing communication environments and improve the coverage and quality of wireless communication services. Integrating FL and IRS into drones offers several advantages over traditional wireless communication networks, including rapid deployment in emergencies or disasters, improved coverage and quality of communication services, and increased accessibility to remote areas. Finally, we highlight the challenges and opportunities of integrating FL and IRS into drones for researchers interested in drone networks. We also help drive innovation in developing 6G communication networks.</description><subject>6G mobile communication</subject><subject>Array signal processing</subject><subject>Collaboration</subject><subject>Communication networks</subject><subject>Communications networks</subject><subject>drone swarm</subject><subject>Drones</subject><subject>Energy consumption</subject><subject>Energy costs</subject><subject>Federated learning</subject><subject>IoT</subject><subject>IRS</subject><subject>Particle swarm optimization</subject><subject>Quality of service</subject><subject>Reflection</subject><subject>Signal reflection</subject><subject>Smart devices</subject><subject>smart environment</subject><subject>Wireless communication</subject><subject>Wireless communications</subject><subject>Wireless networks</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUV1rGzEQPEoLDWl-Qfsg6LNdfd-pb-HqpAangTh5FpJuz5V7lVxJJuTfR-6FkH3ZZZiZ3WWa5jPBS0Kw-nbZ96vtdkkxZUvGKGNKvWvOKJFqwQST79_MH5uLnPe4Vlch0Z41T1cwQDIFBrQBk4IPO3QDUDJahwLT5HcQHKA7GCdwxceAtsc0mgr5gH6kGCCjMSa0CsZOJ7G8Rr-gPMb0J39H_W8zTRB2lWTCgG4Ph5jKMfjiIX9qPoxmynDx0s-bh6vVff9zsbm9XveXm4XjWJUFocpapiQo2nLiOPBBGM6gUwOoznaDFcQ5ix1usRKcSDxa04IithuNES07b9az7xDNXh-S_2vSk47G6_9ATDttUvFuAt1JIZiVAixVvJW8rpBjC6CA0kF2tnp9nb0OKf47Qi56H48p1PM17ZTAmAlOK4vNLJdizgnG160E61Nkeo5MnyLTL5FV1ZdZ5QHgjaK-rVrOngGZu5MH</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Shvetsov, Alexey V.</creator><creator>Alsamhi, Saeed Hamood</creator><creator>Hawbani, Ammar</creator><creator>Kumar, Santosh</creator><creator>Srivastava, Sumit</creator><creator>Agarwal, Sweta</creator><creator>Rajput, Navin Singh</creator><creator>Alammari, Amr A.</creator><creator>Nashwan, Farhan M. 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Furthermore, integrating IRS with Federated Learning (FL) can further boost the performance of the drone network by enabling collaborative learning among multiple drones, leading to better and more efficient decision-making and holding great promise for enabling 6G communication networks. Therefore, we present a novel framework for FL meets IRS in drones for enabling 6G. In this framework, multiple IRS-equipped drone swarm are deployed to form a distributed wireless network, where FL techniques are used to collaborate with the learning process and optimize the reflection coefficients of each drone-IRS. This allows drone swarm to adapt to changing communication environments and improve the coverage and quality of wireless communication services. Integrating FL and IRS into drones offers several advantages over traditional wireless communication networks, including rapid deployment in emergencies or disasters, improved coverage and quality of communication services, and increased accessibility to remote areas. Finally, we highlight the challenges and opportunities of integrating FL and IRS into drones for researchers interested in drone networks. We also help drive innovation in developing 6G communication networks.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2023.3323399</doi><tpages>28</tpages><orcidid>https://orcid.org/0000-0002-1650-011X</orcidid><orcidid>https://orcid.org/0000-0002-6634-1727</orcidid><orcidid>https://orcid.org/0000-0003-2857-6979</orcidid><orcidid>https://orcid.org/0000-0002-1069-3993</orcidid><orcidid>https://orcid.org/0000-0001-8166-6127</orcidid><orcidid>https://orcid.org/0000-0003-2264-9014</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 6G mobile communication Array signal processing Collaboration Communication networks Communications networks drone swarm Drones Energy consumption Energy costs Federated learning IoT IRS Particle swarm optimization Quality of service Reflection Signal reflection Smart devices smart environment Wireless communication Wireless communications Wireless networks |
title | Federated Learning Meets Intelligence Reflection Surface in Drones for Enabling 6G Networks: Challenges and Opportunities |
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