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RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication.
- Source :
-
Sensors (14248220) . Jul2022, Vol. 22 Issue 13, p4843-N.PAG. 11p. - Publication Year :
- 2022
-
Abstract
- Unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) technology can simultaneously offer flexible communications and illumination to service ground users. Since a poor UAV working environment increases interference sent to the VLC link, there is a pressing need to further ensure reliable data communications. Run-length limited (RLL) codes are commonly utilized to ensure reliable data transmission and flicker-free perception in VLC technology. Conventional RLL decoding methods depend upon look-up tables, which can be prone to erroneous transmissions. This paper proposes a novel recurrent neural network (RNN)-based decoder for RLL codes that uses sequence to sequence (seq2seq) models. With a well-trained model, the decoder has a significant performance advantage over the look-up table method, and it can approach the bit error rate of maximum a posteriori (MAP) criterion-based decoding. Moreover, the decoder is use to deal with multiple frames simultaneously, such that the totality of RLL-coded frames can be decoded by only one-shot decoding within one time slot, which is able to enhance the system throughput. This shows our decoder's great potential for practical UAV applications with VLC technology. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 22
- Issue :
- 13
- Database :
- Academic Search Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 157994373
- Full Text :
- https://doi.org/10.3390/s22134843