An RNN-based decoding scheme for IM/DD visible light communication systems
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Từ khóa:
VLC
RNN
LSTM
FEC decoding
visible light communication
Tóm tắt
In Visible Light Communication (VLC) systems, the integration of Forward Error Correction (FEC) and Run-Length Limited (RLL) coding is essential for ensuring transmission reliability and flicker-free operation. Despite their efficacy, traditional joint decoding schemes often suffer from prohibitive computational overhead. This paper introduces a Deep Learning-based decoding framework designed to replace the conventional concatenated RLL-FEC decoding chain. The proposed architecture employs a single-layer Recurrent Neural Network (RNN) followed by a dense fully-connected layer. Furthermore, a confidence-bit demodulation strategy is implemented to refine the input features for the RNN. Empirical results demonstrate that for short-packet scenarios, the proposed RNN-centric decoder achieves error-correction performance on par with state-of-the-art benchmarks while delivering a significant leap in processing throughput.
Tài liệu tham khảo
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Dinh, D. L., T. N. Van, và P. P. Thanh. “An RNN-Based Decoding Scheme for IM/DD Visible Light Communication Systems”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 24, số p.h 1, Tháng Hai 2026, tr 41-47, doi:10.31130/ud-jst.2026.24(1).596E.
