<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>In recent years, artificial neural networks (ANNs) have grown a lot and helped solve numerous problems in wireless communication systems. We have evaluated the performance of the Bidirectional-Long-Short-Term-Memory (Bi-LSTM) recurrent neural networks (RNNs) for joint blind channel equalization and symbol detection using a variety of activation functions (Afs) for the gate units (sigmoid) of Bi-LSTMs without requiring any prior knowledge of channel state information (CSI). The performance of Bi-LSTM networks with different AFs found in the literature is compared. This comparison was carried out with the assistance of three different learning algorithms, namely Adam, rmsprop, and SGdm. The research findings clearly show that performance, as measured by equalization accuracy, can be improved. Furthermore, demonstrate that the sigmoid gate activation function (GAF), which is commonly used in Bi-LSTMs, does not significantly contribute to optimal network behavior. In contrast, there are a great many less well-known AFs that are capable of outperforming the ones that are most frequently utilized. </p>
</abstract>

In recent years, artificial neural networks (ANNs) have grown a lot and helped solve numerous problems in wireless communication systems. We have evaluated the performance of the Bidirectional-Long-Short-Term-Memory (Bi-LSTM) recurrent neural networks (RNNs) for joint blind channel equalization and symbol detection using a variety of activation functions (Afs) for the gate units (sigmoid) of Bi-LSTMs without requiring any prior knowledge of channel state information (CSI). The performance of Bi-LSTM networks with different AFs found in the literature is compared. This comparison was carried out with the assistance of three different learning algorithms, namely Adam, rmsprop, and SGdm. The research findings clearly show that performance, as measured by equalization accuracy, can be improved. Furthermore, demonstrate that the sigmoid gate activation function (GAF), which is commonly used in Bi-LSTMs, does not significantly contribute to optimal network behavior. In contrast, there are a great many less well-known AFs that are capable of outperforming the ones that are most frequently utilized.

Modified gate activation functions of Bi-LSTM-based SC-FDMA channel equalization 

Department of Electrical Engineering, Faculty of Engineering

Aswan Faculty of Engineering, Aswan University

Journal of Electrical Engineering

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

{"article-title":"Modified gate activation functions of Bi-LSTM-based SC-FDMA channel equalization"}

In recent years, artificial neural networks (ANNs) have grown a lot and helped solve numerous problems in wireless communication systems. We have evaluated the...

Modified gate activation functions of Bi-LSTM-based SC-FDMA channel equalization

Publicado en línea: 29 ago 2023

Páginas: 256 - 266

Recibido: 28 mar 2023

DOI: https://doi.org/10.2478/jee-2023-0032

Palabras clave
artificial neural networks, activation functions, channel equalization, symbol detection, bidirectional long short-term memory, recurrent neural networks

© 2023 Mohamed A. Mohamed et al., published by Sciendo

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

Modified gate activation functions of Bi-LSTM-based SC-FDMA channel equalization

Publicado en línea: 29 ago 2023

Páginas: 256 - 266

Recibido: 28 mar 2023

DOI: https://doi.org/10.2478/jee-2023-0032

Palabras claveartificial neural networks, activation functions, channel equalization, symbol detection, bidirectional long short-term memory, recurrent neural networks

© 2023 Mohamed A. Mohamed et al., published by Sciendo

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

Palabras clave
artificial neural networks, activation functions, channel equalization, symbol detection, bidirectional long short-term memory, recurrent neural networks