Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/180158
Title: Intelligent reflecting surfaces-assisted hybrid THz/RF system over generalized fading
Authors: Premanand, Rithwik
Vishwakarma, Narendra
Singh, Ranjan
Madhukumar, A. S.
Keywords: Engineering
Issue Date: 2024
Source: Premanand, R., Vishwakarma, N., Singh, R. & Madhukumar, A. S. (2024). Intelligent reflecting surfaces-assisted hybrid THz/RF system over generalized fading. IEEE International Conference on Communications (ICC 2024), 5534-5539. https://dx.doi.org/10.1109/ICC51166.2024.10622184
Project: NRF-CRP23-2019-0005
FCP-NTU-RG-2022-014
Conference: IEEE International Conference on Communications (ICC 2024)
Abstract: Intelligent reflecting surface (IRS) technology is a promising solution for addressing the limitations of terahertz (THz) systems, including blockage effects and tremendous path loss. This paper investigates a novel IRS-assisted hybrid framework that seamlessly combines both THz and radio frequency (RF) technologies using a selection combining (SC) scheme, thereby enhancing system reliability. The study incorporates the deterministic and statistical properties of IRS in both RF and THz domains employing a sophisticated spatial scattering chan-nel model across generalized α - µ fading channels. Specifically, the exact closed-form expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the output signal-to-noise ratio (SNR) are derived for both THz and RF links. From this, the outage probability and average symbol error rate (SER) are derived. Furthermore, asymptotic expressions are evaluated for outage and average SER, offering insights into diversity gain of the system. Simulation results reveal a significant enhancement in system performance and power savings for the proposed IRS-aided hybrid THz/RF system. These findings provide valuable insights into the practical implementation of IRS-assisted hybrid networks, contributing to the ongoing efforts for future wireless networks.
URI: https://hdl.handle.net/10356/180158
ISBN: 978-1-7281-9054-9
ISSN: 1938-1883
DOI: 10.1109/ICC51166.2024.10622184
DOI (Related Dataset): 10.21979/N9/ZRTSHW
Schools: College of Computing and Data Science 
Rights: © 2024 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CCDS Conference Papers

SCOPUSTM   
Citations 50

1
Updated on Apr 30, 2025

Page view(s)

119
Updated on May 6, 2025

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.