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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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An innovative Squid Game Optimizer for enhanced channel estimation and massive MIMO detection using dilated adaptive

G Navabharat Reddy1, C V Ravikumar2, Oliver Takacs3

  • 1Physical Design Engineer, Wafersemiconductors Technologies Pvt Ltd, Bangalore, India.

Scientific Reports
|August 29, 2025
PubMed
Summary

This study introduces a deep learning network for efficient massive Multiple-Input Multiple-Output (MIMO) detection and channel estimation. The novel approach significantly reduces computational complexity for advanced wireless communication systems.

Keywords:
Channel estimationDilated adaptive recurrent neural network with attention mechanismMassive MIMO detectionModified Squid Game Optimizer

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Area of Science:

  • Wireless Communication
  • Signal Processing
  • Deep Learning

Background:

  • Conventional signal recognition in Multiple-Input Multiple-Output (MIMO) systems faces computational challenges with increasing antennas and modulation orders.
  • Deep learning offers a promising solution due to its versatility, nonlinear modeling, and parallel computation efficiency for large-scale MIMO detection.

Purpose of the Study:

  • To develop a deep learning network for channel estimation and massive MIMO detection that reduces computational complexity.
  • To enhance channel capacity and optimize detection performance in MIMO systems.

Main Methods:

  • Channel estimation using a confusion matrix and the Modified Squid Game Optimizer (MSGO).
  • Massive MIMO detection via Dilated Adaptive Recurrent Neural Network with Attention Mechanism (DARNN-AM).
  • Optimization of DARNN-AM using MSGO for fine-tuning network attributes.

Main Results:

  • The proposed deep learning network effectively reduces computational complexity in massive MIMO detection.
  • The system demonstrates superior performance compared to existing techniques, validated through comparative analysis.
  • The network achieves multi-segment mapping across various modulation schemes.

Conclusions:

  • The developed deep learning approach provides an efficient solution for channel estimation and massive MIMO detection.
  • The integration of MSGO and DARNN-AM optimizes performance and reduces computational load.
  • This method offers a significant advancement for future high-performance wireless communication systems.