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RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication.

Xu Luo1, Haifen Yang1

  • 1Department of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

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|July 9, 2022
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Summary
This summary is machine-generated.

This study introduces a new recurrent neural network (RNN) decoder for run-length limited (RLL) codes in unmanned aerial vehicle (UAV) visible light communication (VLC) systems. The novel decoder enhances data reliability and system throughput for practical UAV applications.

Keywords:
RNN-based decodingrun-length limited codesseq2seq modelunmanned aerial vehiclesvisible light communication

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Area of Science:

  • Wireless Communications
  • Optical Communications
  • Artificial Intelligence

Background:

  • Unmanned aerial vehicles (UAVs) with visible light communication (VLC) offer flexible communication and illumination.
  • Interference in UAV environments degrades VLC link reliability.
  • Run-length limited (RLL) codes are crucial for reliable data transmission and flicker-free perception in VLC.

Purpose of the Study:

  • To develop a novel decoder for RLL codes to improve data reliability in UAV-based VLC systems.
  • To overcome limitations of conventional look-up table RLL decoding methods.
  • To enhance system throughput by enabling simultaneous decoding of multiple frames.

Main Methods:

  • Proposed a novel recurrent neural network (RNN)-based decoder utilizing sequence-to-sequence (seq2seq) models.
  • Trained the RNN model for decoding RLL codes.
  • Implemented a one-shot decoding approach for multiple RLL-coded frames within a single time slot.

Main Results:

  • The proposed RNN decoder significantly outperforms conventional look-up table methods.
  • Achieved a bit error rate comparable to maximum a posteriori (MAP) criterion-based decoding.
  • Demonstrated enhanced system throughput through simultaneous multi-frame decoding.

Conclusions:

  • The novel RNN-based decoder offers a significant performance advantage for RLL codes in VLC systems.
  • The decoder shows great potential for reliable and efficient data communication in practical UAV applications.
  • The seq2seq model approach provides a robust solution for interference challenges in UAV VLC environments.