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OTFS通信システムの分離可能なCNNに基づく信号検出

Ying Wang1, Zixu Zhang2, Hang Li1

  • 1The School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China.

Entropy (Basel, Switzerland)
|August 28, 2025
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まとめ

この研究は,オートゴーナル・タイム・周波数・スペース (OTFS) システムの低複雑性信号検出方法であるSeCNN-OTFSを導入します. 非常に少ないパラメータで高い性能を達成し,リソースが限られた通信システムに最適です.

キーワード:
折り畳み神経ネットワークオートゴーナルタイム周波数空間シグナル検出

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科学分野:

  • ワイヤレス通信
  • 信号処理
  • 機械学習

背景:

  • オートゴーナル・タイム・周波数空間 (OTFS) 調節は,高ドップラー環境で利点を提供しています.
  • OTFSシステムにおける従来の信号検出方法は,コンピューティングの複雑性と性能に問題があります.
  • ディープラーニングのアプローチ,特にコンボリューションニューラルネットワーク (CNN) は有望ですが,資源密集的です.

研究 の 目的:

  • OTFSシステムの低複雑で効率的な信号検出方法を開発する.
  • 高ドップラー条件での特徴的差別と訓練の安定性を強化する.
  • OTFS信号検出の計算上のオーバーヘッドを減らし,実用的な展開のために.

主な方法:

  • SeCNN-OTFSと呼ばれる新しい分離可能なコンボリューションニューラルネットワーク (SeCNN) のアーキテクチャを提案した.
  • SeparableBlock内の統合された残留接続とチャネル注意メカニズム.
  • 複雑性を減らすために深度操作と点操作に分解した.

主要な成果:

  • SeCNN-OTFSは最小正方形 (LS) と最小正方形誤差 (MMSE) 推定器よりも優れたパフォーマンスを示しました.
  • 信号対ノイズ比 (SNR) が12.5dB以上で,2D-CNNとほぼ同一のビットエラーレート (BER) の性能を達成した.
  • 標準の2D-CNNと比較して,パラメータの19%しか必要とされず,複雑性が大幅に減少したことを示しています.

結論:

  • SeCNN-OTFSは,OTFSシステムにおけるシグナル検出に有効で計算効率の高いソリューションを提供します.
  • この方法は,衛星やモノのインターネット (IoT) 通信などのリソースが限られたアプリケーションに非常に適しています.
  • 十分なリソースでより高い精度を要求するシナリオでは,従来のコンボリューションレイヤのバリエーションが利用できます.