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Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications System.

Belayneh Abebe Tesfaw¹, Rong-Terng Juang², Li-Chia Tai³

¹Department of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, Taiwan.

Sensors (Basel, Switzerland)

|October 14, 2023

Summary

This summary is machine-generated.

This study introduces a new model using a gated recurrent unit (GRU) to accurately estimate wireless link quality for ground users in drone-assisted communication networks aided by reconfigurable intelligent surfaces (RIS). The model enhances performance in complex environments.

Keywords:

gated recurrent unit (GRU)link quality estimation reconfigurable intelligent surfaces (RIS)unmanned aerial vehicle (UAV)

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Area of Science:

Wireless Communication Engineering
Artificial Intelligence in Networks
Aerial Communication Systems

Background:

Unmanned Aerial Vehicles (UAVs) are increasingly used for wireless communication, but face challenges in urban environments due to power limitations and non-line-of-sight (NLOS) connections.
Reconfigurable Intelligent Surfaces (RIS) offer a promising solution to improve UAV communication by optimizing signal propagation.
Estimating link quality for ground users in dynamic RIS-assisted UAV networks remains a significant challenge.

Purpose of the Study:

To propose a novel link quality estimation model for multi-user RIS-assisted UAV-enabled wireless communication systems.
To accurately assess the communication link quality for individual ground users in dynamic and complex environments.
To leverage advanced machine learning techniques for performance enhancement in aerial communication networks.

Main Methods:

Development of a link quality estimation model utilizing a Gated Recurrent Unit (GRU), a type of recurrent neural network.
The GRU model processes time-series data including user channel information and RIS phase shift configurations.
Simulation of a multi-user RIS-assisted UAV-enabled wireless communication system to validate the model's efficacy.

Main Results:

The proposed GRU-based model demonstrated effective and accurate estimation of ground user link quality.
The framework successfully handled the complexities of dynamic RIS configurations and UAV mobility.
Simulation results confirmed the model's capability in enhancing communication performance prediction.

Conclusions:

The GRU model provides a robust solution for link quality estimation in challenging RIS-assisted UAV communication scenarios.
Accurate link quality estimation is crucial for optimizing resource allocation and ensuring reliable communication.
This research contributes to the advancement of intelligent aerial communication networks through AI-driven channel assessment.