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拡張適応RNNを使用した拡張チャネル推定と大規模なMIMO検出のための革新的なSquid Game Optimizer

G Navabharat Reddy¹, C V Ravikumar², Oliver Takacs³

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

Scientific reports

|August 29, 2025

まとめ

この研究は,効率的な大規模な複数入力複数出力 (MIMO) 検出とチャネル推定のためのディープラーニングネットワークを導入します. この新しいアプローチは,高度なワイヤレス通信システムの計算の複雑さを大幅に削減します.

科学分野:

ワイヤレス通信
シグナル処理
深層学習

背景:

多入力多出力 (MIMO) システムにおける従来の信号認識は,アンテナと変調順序の増加により計算上の課題に直面しています.
ディープラーニングは,その汎用性,非線形モデリング,および大規模なMIMO検出のための並列計算効率により有望なソリューションを提供します.

研究の目的:

チャネル推定と大規模なMIMO検出のためのディープラーニングネットワークを開発し,計算の複雑さを軽減します.
MIMOシステムでチャネル容量を高め,検出性能を最適化する.

主な方法:

混同マトリックスとModified Squid Game Optimizer (MSGO) を使用してチャネルを推定する.
注意力メカニズム (DARNN-AM) による大規模なMIMO検出
ネットワーク属性を微調整するためにMSGOを使用してDARNN-AMの最適化.

主要な成果:

提案されたディープラーニングネットワークは,大規模なMIMO検出における計算の複雑さを効果的に削減します.
このシステムは,比較分析によって検証された既存の技術と比較して優れた性能を示しています.
ネットワークは,様々なモジュレーションスキームでマルチセグメントマッピングを達成します.

結論:

開発されたディープラーニングアプローチは,チャネル推定と大規模なMIMO検出のための効率的なソリューションを提供します.
MSGOとDARNN-AMの統合はパフォーマンスを最適化し,コンピューティング負荷を軽減します.
この方法は将来の高性能無線通信システムに重要な進歩をもたらします

キーワード:

チャンネル推定注意力メカニズムを備えた拡張適応性再発性ニューラルネットワーク大量のMIMO検出修正されたスクラッドゲーム最適化プログラム

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