<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>One of the most important tasks to be considered in wireless communication systems, especially in multi-carrier systems such as Multi-Input Multi Output Non-Orthogonal (MIMO-NOMA), is to correctly estimate the channel state information for coherent detection at the receiver. A hybrid deep learning model, called convolutional fuzzy deep neural networks, is proposed in this study for accurately estimating channel state information and detecting symbols in MIMO-NOMA systems. The performance of this proposal has been compared to traditional algorithms like Least Square Error- Successive Interference Cancelation (LS-SIC) and linear minimum mean square (LMMSE-SIC), as well as to other deep learning methods such as convolutional neural networks. With this proposed scheme, significantly less bit error rate is obtained in both Rician and Rayleigh channel environment compared to other algorithms. In addition to the high performance of this scheme, the fact that it does not need channel statistics is another important advantage.</p>
</abstract>

One of the most important tasks to be considered in wireless communication systems, especially in multi-carrier systems such as Multi-Input Multi Output Non-Orthogonal (MIMO-NOMA), is to correctly estimate the channel state information for coherent detection at the receiver. A hybrid deep learning model, called convolutional fuzzy deep neural networks, is proposed in this study for accurately estimating channel state information and detecting symbols in MIMO-NOMA systems. The performance of this proposal has been compared to traditional algorithms like Least Square Error- Successive Interference Cancelation (LS-SIC) and linear minimum mean square (LMMSE-SIC), as well as to other deep learning methods such as convolutional neural networks. With this proposed scheme, significantly less bit error rate is obtained in both Rician and Rayleigh channel environment compared to other algorithms. In addition to the high performance of this scheme, the fact that it does not need channel statistics is another important advantage.

Convolutional fuzzy neural network based symbol detection in MIMO NOMA systems

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Convolutional fuzzy neural network based symbol detection in MIMO NOMA systems"}

One of the most important tasks to be considered in wireless communication systems, especially in multi-carrier systems such as Multi-Input Multi Output...

Convolutional fuzzy neural network based symbol detection in MIMO NOMA systems

Article Category: PAPERS

Publié en ligne: 07 mars 2023

Pages: 70 - 74

Reçu: 24 janv. 2023

DOI: https://doi.org/10.2478/jee-2023-0009

Mots clés
MIMO-NOMA, channel estimation, symbol detection, convolutional fuzzy neural network

© 2023 Muhammet Nuri Seyman, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Convolutional fuzzy neural network based symbol detection in MIMO NOMA systems

Article Category: PAPERS

Publié en ligne: 07 mars 2023

Pages: 70 - 74

Reçu: 24 janv. 2023

DOI: https://doi.org/10.2478/jee-2023-0009

Mots clésMIMO-NOMA, channel estimation, symbol detection, convolutional fuzzy neural network

© 2023 Muhammet Nuri Seyman, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
MIMO-NOMA, channel estimation, symbol detection, convolutional fuzzy neural network