Deep learning based repetition coding for MIMO VLC systems

Wessam M. Salama

doi:10.1007/s11082-025-08317-7

Deep learning based repetition coding for MIMO VLC systems

2025-07-11

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DOI：10.1007/s11082-025-08317-7

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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.

Keywords:

Deep learning (DL)

Machine learning (ML)

Multiple input multiple output (MIMO)

Multi-path feature fusion single shot multi-box detector (MF-SSD)

Redundancy coding (RC)

Deep learning based repetition coding for MIMO VLC systems

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