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<p>In this paper, an enhanced model of Optical Wireless Communications (OWC) is investigated utilizing Multiple Input Multiple Output (MIMO) strategies. The non-Lambertian light pillars, explicitly from economically accessible LEDs are applied to arrange MIMO-OWC joints in commonplace indoor conditions. Both homogeneous and heterogeneous non-Lambertian MIMO arrangements are examined. To assess the spatial convergence execution, a low-intricacy Redundancy Coding (RC) MIMO calculation is utilized. Moreover, combining the Single Shot Multi-Box Detector (SSD) model with the AdaBoost machine Learning (ML) model is implemented to enhance the performance and reliability of these techniques. In that context, a Multi-Path Feature Fusion Single Shot Multi-Box Detector (MF-SSD) is proposed. A new detection network is used with an effective feature fusion module based on SSD and DenseNet121 as a backbone. Two recently created modules with dilated convolution make up the suggested feature fusion module, which fuses features from shallow layers, which primarily contain boundary information, to higher-level features which primarily contain semantic reach information without lowering the feature map initial resolution. Furthermore, the model is fine-tuned to be suitable for our prediction task. Furthermore, K-fold cross validation is utilized to overcome the overfitting and evaluate the non- trainable parameters in the proposed framework. The MF-SSD model outperforms the SSD-AdaBoost model in all different scenarios typical to the kind of the transmitters.</p>

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Deep learning based repetition coding for MIMO VLC systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月11日

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  • Wessam M. Salama,
  • Moustafa H. Aly,
  • Eman S. Amer

摘要

In this paper, an enhanced model of Optical Wireless Communications (OWC) is investigated utilizing Multiple Input Multiple Output (MIMO) strategies. The non-Lambertian light pillars, explicitly from economically accessible LEDs are applied to arrange MIMO-OWC joints in commonplace indoor conditions. Both homogeneous and heterogeneous non-Lambertian MIMO arrangements are examined. To assess the spatial convergence execution, a low-intricacy Redundancy Coding (RC) MIMO calculation is utilized. Moreover, combining the Single Shot Multi-Box Detector (SSD) model with the AdaBoost machine Learning (ML) model is implemented to enhance the performance and reliability of these techniques. In that context, a Multi-Path Feature Fusion Single Shot Multi-Box Detector (MF-SSD) is proposed. A new detection network is used with an effective feature fusion module based on SSD and DenseNet121 as a backbone. Two recently created modules with dilated convolution make up the suggested feature fusion module, which fuses features from shallow layers, which primarily contain boundary information, to higher-level features which primarily contain semantic reach information without lowering the feature map initial resolution. Furthermore, the model is fine-tuned to be suitable for our prediction task. Furthermore, K-fold cross validation is utilized to overcome the overfitting and evaluate the non- trainable parameters in the proposed framework. The MF-SSD model outperforms the SSD-AdaBoost model in all different scenarios typical to the kind of the transmitters.