Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/4757
Title: | Reconfigurable Intelligent Surface-Assisted OFDM-IM for beyond 5G Mobile Networks: ML and LLR Detector Designs | Authors: | Ceniklioglu, B. Develi, I. Canbilen, A.E. |
Keywords: | index modulation (IM) log-likelihood ratio (LLR) maximum likelihood (ML) Orthogonal frequency division multiplexing (OFDM) reconfigurable intelligent surface (RIS) 5G mobile communication systems Orthogonal frequency division multiplexing Quality of service Index modulation Log likelihood ratio Log-likelihood ratio Ma ximum likelihoods Maximum likelihood Maximum-likelihood Orthogonal frequency division multiplexing Orthogonal frequency-division multiplexing Reconfigurable Reconfigurable intelligent surface Maximum likelihood |
Issue Date: | 2023 | Publisher: | Institute of Electrical and Electronics Engineers Inc. | Abstract: | The several countries of the world is intensified their investigative on the fifth generation and beyond (5GB) communications in order to reach the new requirements for new wireless applications. In this context, orthogonal frequency division multiplexing with index modulation (OFDM-IM) concept is suggested as one of the ideal solutions in the literature. Additionally, reconfigurable intelligent surfaces (RISs) can be occupied to enahance the quality of service (QoS). Considering that, the performance of RIS-assisted OFDM-IM system is examined by applying maximum likelihood (ML) and log-likelihood ratio (LLR) detectors in this paper. It is observed from the provided computer simulation results that integrating an RIS consisting of many low-cost and passive elements, into OFDM-IM systems considerably increase the overall system performance. © 2023 IEEE. | Description: | 1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023 -- 21 April 2023 through 22 April 2023 -- -- 193100 | URI: | https://doi.org/10.1109/InC457730.2023.10262978 https://hdl.handle.net/20.500.13091/4757 |
ISBN: | 9798350335774 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections |
Show full item record
CORE Recommender
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.