Federated Learning Meets Intelligence Reflection Surface in Drones for Enabling 6G Networks: Challenges and Opportunities

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Abstract

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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APA

Shvetsov, A. V., Alsamhi, S. H., Hawbani, A., Kumar, S., Srivastava, S., Agarwal, S., … Nashwan, F. M. A. (2023). Federated Learning Meets Intelligence Reflection Surface in Drones for Enabling 6G Networks: Challenges and Opportunities. IEEE Access, 11, 130860–130887. https://doi.org/10.1109/ACCESS.2023.3323399

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