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https://hdl.handle.net/10356/179676
Title: | RIS-assisted hybrid FSO/THz system with diversity combining schemes: a performance analysis | Authors: | Vishwakarma, Narendra Swaminathan, R. Premanand, Rithwik Sharma, Shubha Madhukumar, A. S. |
Keywords: | Engineering | Issue Date: | 2024 | Source: | Vishwakarma, N., Swaminathan, R., Premanand, R., Sharma, S. & Madhukumar, A. S. (2024). RIS-assisted hybrid FSO/THz system with diversity combining schemes: a performance analysis. IEEE Internet of Things Journal. https://dx.doi.org/10.1109/JIOT.2024.3405973 | Project: | NRF-CRP23- 2019-0005 FCP-NTU-RG-2022-014 |
Journal: | IEEE Internet of Things Journal | Abstract: | This paper investigates a hybrid framework that combines free space optics (FSO) and terahertz (THz) technologies for a backhaul network. In this hybrid configuration, the FSO and THz systems are empowered by reconfigurable intelligent surface (RIS) and are combined at the receiver using either maximal ratio combining (MRC) or selection combining (SC). The performance of the RIS-assisted hybrid FSO/THz system is evaluated under multiple impairments for FSO and THz links. The exact closed-form expressions for probability density function (PDF), cumulative distribution function (CDF), and moment-generating function (MGF) of the output signal-to-noise ratio (SNR) of both RIS-assisted FSO and THz links are derived. Leveraging these SNR statistics, we compute the outage probability and the average symbol error rate (SER) of the proposed hybrid FSO/THz system with MRC and SC schemes. Additionally, this paper conducts an asymptotic analysis at high SNR levels, yielding asymptotic expressions for outage and average SER, which not only reduce computational complexity but also provide insights into the system’s diversity gain. In the numerical results, the performance of the proposed hybrid FSO/THz system is compared against other state-of-the-art wireless systems, including direct link FSO without RIS, RIS-aided FSO, and the hybrid FSO/THz systems without RIS using diversity combining techniques. | URI: | https://hdl.handle.net/10356/179676 | ISSN: | 2327-4662 | DOI: | 10.1109/JIOT.2024.3405973 | Schools: | College of Computing and Data Science School of Computer Science and Engineering |
Rights: | © 2024 IEEE. All rights reserved. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | CCDS Journal Articles |
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