Deep Learning-Enhanced Autoencoder for Multi-Carrier Wireless Systems

Md Abdul Aziz, Md Habibur Rahman, Rana Tabassum, Mohammad Abrar Shakil Sejan, Myung-Sun Baek and Hyoung-Kyu Song ()
Additional contact information
Md Abdul Aziz: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Md Habibur Rahman: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Rana Tabassum: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Mohammad Abrar Shakil Sejan: Department of Electrical Engineering, Sejong University, Seoul 05006, Republic of Korea
Myung-Sun Baek: Department of Electrical Engineering, Sejong University, Seoul 05006, Republic of Korea
Hyoung-Kyu Song: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea

Mathematics, 2024, vol. 12, issue 23, 1-19

Abstract: In a multi-carrier (MC) system, the transmitted data are split across several sub-carriers as a crucial approach for achieving high data rates, reliability, and spectral efficiency. Deep learning (DL) enhances MC systems by improving signal representation, leading to more efficient data transmission and reduced bit error rates. In this paper, we propose an MC system supported by DL for operation on fading channels. Deep neural networks are utilized to model the modulation block, while a gated recurrent unit (GRU) network is used to model the demodulation blocks, acting as the encoder and decoder within an autoencoder (AE) architecture. The proposed scheme, known as MC-AE, differs from existing AE-based systems by directly processing channel state information and the received signal in a fully data-driven way, unlike traditional methods that rely on channel equalizers. This approach enables MC-AE to improve diversity and coding gains in fading channels by simultaneously optimizing the encoder and decoder. In this experiment, we evaluated the performance of the proposed model under both perfect and imperfect channel conditions and compared it with other models. Additionally, we assessed the performance of the MC-AE system against index modulation-based MC systems. The results demonstrate that the GRU-based MC-AE system outperforms the others.

Keywords: multicarrier; autoencoder; deep learning; wireless communication; block error rate; gated recurrent unit (GRU); modulation; demodulation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/23/3685/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/23/3685/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:23:p:3685-:d:1528351

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().