Channel tracking in IRS-based UAV communication systems using federated learning

This paper aims to overcome the problems and limitations of the communications of Unmanned Aerial Vehicles (UAV) by incorporating Intelligent Reflecting Surface (IRS) into UAV for channel tracking. Since IRS may change the propagation environment, is a desirable option for combining with UAV to impr...

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Bibliographic Details
Published in:Journal of Electrical Engineering 2023-12, Vol.74 (6), p.521-531
Main Authors: Sharma, Itika, Gupta, Sachin Kumar
Format: Article
Language:English
Subjects:
Artificial intelligence
channel tracking
Communication
Communications systems
Confidentiality
Deep learning
Drones
Federated learning
IRS
LSTM
Machine learning
Tracking
UAV
Unmanned aerial vehicles
Wireless networks
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Summary:This paper aims to overcome the problems and limitations of the communications of Unmanned Aerial Vehicles (UAV) by incorporating Intelligent Reflecting Surface (IRS) into UAV for channel tracking. Since IRS may change the propagation environment, is a desirable option for combining with UAV to improve wireless network security. Due to its capacity to proactively configure the wireless environment, IRS technology is a potential one for future communication systems. IRS is able to provide steady communications and serve a greater coverage area by reflecting signals to create virtual LoS routes. Moreover, we develop a federated learning-based channel tracking technique in which federated learning is used to determine the security and pre-estimation constituent. In addition, for channel tracking, Long Short-Term Memory (LSTM) is developed. Due to their ability to understand long-term connections between data time steps, LSTMs are frequently used to learn, analyze, and classify sequential data.
ISSN:1339-309X
1335-3632
1339-309X
DOI:10.2478/jee-2023-0060