An innovative Squid Game Optimizer for enhanced channel estimation and massive MIMO detection using dilated adaptive RNNs

The Multiple-Input Multiple-Output (MIMO) system can provide improved spectral efficiency and energy performance. However, the computational demand faced by conventional signal recognition techniques has significantly increased due to the growing number of antennas and higher-order modulations. To o...

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Vydané v:	Scientific reports Ročník 15; číslo 1; s. 31921 - 33
Hlavní autori:	Reddy, G. Navabharat, Ravikumar, C. V., Takacs, Oliver, Tolba, Amr
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 29.08.2025 Nature Publishing Group Nature Portfolio
Predmet:	639/166 639/4077 Algorithms Antennas Channel estimation Communication Communications systems Computer applications Deep learning Design Dilated adaptive recurrent neural network with attention mechanism Efficiency Humanities and Social Sciences Massive MIMO detection Modified Squid Game Optimizer multidisciplinary Neural networks Science Science (multidisciplinary) Sensors Modified Squid Game Optimizer Channel estimation Massive MIMO detection Dilated adaptive recurrent neural network with attention mechanism
ISSN:	2045-2322, 2045-2322
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Abstract	The Multiple-Input Multiple-Output (MIMO) system can provide improved spectral efficiency and energy performance. However, the computational demand faced by conventional signal recognition techniques has significantly increased due to the growing number of antennas and higher-order modulations. To overcome these challenges, deep learning approaches are adopted as they offer versatility, nonlinear modelling capabilities, and parallel computation efficiency for large-scale MIMO detection. Therefore, a deep network for channel estimation and massive MIMO detection is developed to reduce computational complexity issues. Initially, a channel estimation scheme is developed to enhance the channel capacity of the MIMO system. It correlates the transmitted and received signals using a confusion matrix. The proposed Modified Squid Game Optimizer (MSGO) is employed for channel state estimation. Based on the obtained channel state information, MIMO detection is performed within the communication system. Here, Multiuser Interference Cancellation (MIC)-based iterative sequential detection is initially conducted. Then, massive MIMO detection is performed using the Dilated Adaptive Recurrent Neural Network with Attention Mechanism (DARNN-AM) through learnable parameters. Moreover, to further optimize the detection performance by fine-tuning the attributes of DARNN-AM, the MSGO is utilized. The proposed network performs multi-segment mapping across multiple constellation points with different modulation schemes. The effectiveness of the proposed deep learning-based MIMO detection system is evaluated by comparing it with existing techniques and algorithms to validate its superior performance.
AbstractList	The Multiple-Input Multiple-Output (MIMO) system can provide improved spectral efficiency and energy performance. However, the computational demand faced by conventional signal recognition techniques has significantly increased due to the growing number of antennas and higher-order modulations. To overcome these challenges, deep learning approaches are adopted as they offer versatility, nonlinear modelling capabilities, and parallel computation efficiency for large-scale MIMO detection. Therefore, a deep network for channel estimation and massive MIMO detection is developed to reduce computational complexity issues. Initially, a channel estimation scheme is developed to enhance the channel capacity of the MIMO system. It correlates the transmitted and received signals using a confusion matrix. The proposed Modified Squid Game Optimizer (MSGO) is employed for channel state estimation. Based on the obtained channel state information, MIMO detection is performed within the communication system. Here, Multiuser Interference Cancellation (MIC)-based iterative sequential detection is initially conducted. Then, massive MIMO detection is performed using the Dilated Adaptive Recurrent Neural Network with Attention Mechanism (DARNN-AM) through learnable parameters. Moreover, to further optimize the detection performance by fine-tuning the attributes of DARNN-AM, the MSGO is utilized. The proposed network performs multi-segment mapping across multiple constellation points with different modulation schemes. The effectiveness of the proposed deep learning-based MIMO detection system is evaluated by comparing it with existing techniques and algorithms to validate its superior performance.The Multiple-Input Multiple-Output (MIMO) system can provide improved spectral efficiency and energy performance. However, the computational demand faced by conventional signal recognition techniques has significantly increased due to the growing number of antennas and higher-order modulations. To overcome these challenges, deep learning approaches are adopted as they offer versatility, nonlinear modelling capabilities, and parallel computation efficiency for large-scale MIMO detection. Therefore, a deep network for channel estimation and massive MIMO detection is developed to reduce computational complexity issues. Initially, a channel estimation scheme is developed to enhance the channel capacity of the MIMO system. It correlates the transmitted and received signals using a confusion matrix. The proposed Modified Squid Game Optimizer (MSGO) is employed for channel state estimation. Based on the obtained channel state information, MIMO detection is performed within the communication system. Here, Multiuser Interference Cancellation (MIC)-based iterative sequential detection is initially conducted. Then, massive MIMO detection is performed using the Dilated Adaptive Recurrent Neural Network with Attention Mechanism (DARNN-AM) through learnable parameters. Moreover, to further optimize the detection performance by fine-tuning the attributes of DARNN-AM, the MSGO is utilized. The proposed network performs multi-segment mapping across multiple constellation points with different modulation schemes. The effectiveness of the proposed deep learning-based MIMO detection system is evaluated by comparing it with existing techniques and algorithms to validate its superior performance. Abstract The Multiple-Input Multiple-Output (MIMO) system can provide improved spectral efficiency and energy performance. However, the computational demand faced by conventional signal recognition techniques has significantly increased due to the growing number of antennas and higher-order modulations. To overcome these challenges, deep learning approaches are adopted as they offer versatility, nonlinear modelling capabilities, and parallel computation efficiency for large-scale MIMO detection. Therefore, a deep network for channel estimation and massive MIMO detection is developed to reduce computational complexity issues. Initially, a channel estimation scheme is developed to enhance the channel capacity of the MIMO system. It correlates the transmitted and received signals using a confusion matrix. The proposed Modified Squid Game Optimizer (MSGO) is employed for channel state estimation. Based on the obtained channel state information, MIMO detection is performed within the communication system. Here, Multiuser Interference Cancellation (MIC)-based iterative sequential detection is initially conducted. Then, massive MIMO detection is performed using the Dilated Adaptive Recurrent Neural Network with Attention Mechanism (DARNN-AM) through learnable parameters. Moreover, to further optimize the detection performance by fine-tuning the attributes of DARNN-AM, the MSGO is utilized. The proposed network performs multi-segment mapping across multiple constellation points with different modulation schemes. The effectiveness of the proposed deep learning-based MIMO detection system is evaluated by comparing it with existing techniques and algorithms to validate its superior performance. The Multiple-Input Multiple-Output (MIMO) system can provide improved spectral efficiency and energy performance. However, the computational demand faced by conventional signal recognition techniques has significantly increased due to the growing number of antennas and higher-order modulations. To overcome these challenges, deep learning approaches are adopted as they offer versatility, nonlinear modelling capabilities, and parallel computation efficiency for large-scale MIMO detection. Therefore, a deep network for channel estimation and massive MIMO detection is developed to reduce computational complexity issues. Initially, a channel estimation scheme is developed to enhance the channel capacity of the MIMO system. It correlates the transmitted and received signals using a confusion matrix. The proposed Modified Squid Game Optimizer (MSGO) is employed for channel state estimation. Based on the obtained channel state information, MIMO detection is performed within the communication system. Here, Multiuser Interference Cancellation (MIC)-based iterative sequential detection is initially conducted. Then, massive MIMO detection is performed using the Dilated Adaptive Recurrent Neural Network with Attention Mechanism (DARNN-AM) through learnable parameters. Moreover, to further optimize the detection performance by fine-tuning the attributes of DARNN-AM, the MSGO is utilized. The proposed network performs multi-segment mapping across multiple constellation points with different modulation schemes. The effectiveness of the proposed deep learning-based MIMO detection system is evaluated by comparing it with existing techniques and algorithms to validate its superior performance.
ArticleNumber	31921
Author	Ravikumar, C. V. Tolba, Amr Takacs, Oliver Reddy, G. Navabharat
Author_xml	– sequence: 1 givenname: G. Navabharat surname: Reddy fullname: Reddy, G. Navabharat organization: Physical Design Engineer, Wafersemiconductors Technologies Pvt Ltd – sequence: 2 givenname: C. V. surname: Ravikumar fullname: Ravikumar, C. V. email: cvrkvit@gmail.com organization: School of Electronics Engineering, Vellore Institute of Technology – sequence: 3 givenname: Oliver surname: Takacs fullname: Takacs, Oliver email: oliver.takacs@ddc.sze.hu organization: Digital Development Center, Szechenyi Istvan University – sequence: 4 givenname: Amr surname: Tolba fullname: Tolba, Amr organization: Computer Science Department, Community College, King Saud University
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Keywords	Modified Squid Game Optimizer Channel estimation Massive MIMO detection Dilated adaptive recurrent neural network with attention mechanism
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Snippet	The Multiple-Input Multiple-Output (MIMO) system can provide improved spectral efficiency and energy performance. However, the computational demand faced by... Abstract The Multiple-Input Multiple-Output (MIMO) system can provide improved spectral efficiency and energy performance. However, the computational demand...
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SubjectTerms	639/166 639/4077 Algorithms Antennas Channel estimation Communication Communications systems Computer applications Deep learning Design Dilated adaptive recurrent neural network with attention mechanism Efficiency Humanities and Social Sciences Massive MIMO detection Modified Squid Game Optimizer multidisciplinary Neural networks Science Science (multidisciplinary) Sensors
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Title	An innovative Squid Game Optimizer for enhanced channel estimation and massive MIMO detection using dilated adaptive RNNs
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