Intelligent reflective surfaces in 5G and beyond: optimizing uplink satellite connectivity for IoT

Obidiozor, CO, Sait, A, Al-Hadhrami, T ORCID: https://orcid.org/0000-0001-7441-604X, Alkhammash, EH and Saeed, F, 2025. Intelligent reflective surfaces in 5G and beyond: optimizing uplink satellite connectivity for IoT. PeerJ Computer Science, 11: e2726. ISSN 2376-5992

Full text not available from this repository.

Abstract

In the evolving landscape of communication technologies, the integration of intelligent reflective surfaces (IRS) into uplink satellite communication for Internet of Things (IoT) ecosystems presents a promising solution to overcome traditional communication challenges. The purpose of this study is to explore the impact of IRS on enhancing signal quality and communication efficiency in satellite-supported IoT environments. This article adopts a simulation-based approach, using MATLAB and Simulink to model the uplink transmission of IoT devices to satellites with and without IRS assistance. The methodology focuses on analysing key performance metrics, including signal-to-noise ratio (SNR), spectral efficiency, signal strength, and interference mitigation. A reinforcement learning algorithm was employed to optimise IRS phase shifts and beamforming to maximise communication performance. The findings reveal that the integration of IRS leads to significant improvements in SNR, spectral efficiency, and overall signal quality, with a 2 dB increase in SNR and enhanced data transmission rates compared to non-IRS systems. IRS also mitigates interference and extends the coverage area of satellite networks. These results demonstrate the practical implications of IRS technology, which can be applied in scenarios such as smart cities, remote sensing, and disaster recovery, where reliable satellite communication is crucial. The study highlights the strategic importance of IRS in revolutionising IoT-satellite communication systems and sets the foundation for future work on scaling IRS technology for broader applications.

Item Type:	Journal article
Publication Title:	PeerJ Computer Science
Creators:	Obidiozor, C.O., Sait, A., Al-Hadhrami, T., Alkhammash, E.H. and Saeed, F.
Publisher:	PeerJ
Date:	24 June 2025
Volume:	11
ISSN:	2376-5992
Identifiers:	Number Type 10.7717/peerj-cs.2726 DOI 2459391 Other
Rights:	Distributed under Creative Commons CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	17 Jul 2025 15:17
Last Modified:	17 Jul 2025 15:17
URI:	https://irep.ntu.ac.uk/id/eprint/53976

Actions (login required)

Edit View

Statistics

Views

Downloads