Variable-Length Feedback Codes via Deep Learning

Variable-length feedback coding has the potential to significantly enhance communication reliability in finite block length scenarios by adapting coding strategies based on real-time receiver feedback. Designing such codes, however, is challenging. While deep learning (DL) has been employed to desig...

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Vydané v:IEEE International Conference on Communications (2003) s. 3864 - 3869
Hlavní autori: Lai, Wenwei, Shao, Yulin, Ding, Yu, Gunduz, Deniz
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 08.06.2025
Predmet:
Benchmark testing
Channel coding
Codes
Decoding
Deep learning
Feedback
Receivers
Reliability
Resilience
Symbols
Technological innovation
variable length coding
ISSN:1938-1883
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Shrnutí:Variable-length feedback coding has the potential to significantly enhance communication reliability in finite block length scenarios by adapting coding strategies based on real-time receiver feedback. Designing such codes, however, is challenging. While deep learning (DL) has been employed to design sophisticated feedback codes, existing DL-aided feedback codes are predominantly fixed-length and suffer performance degradation in the high code rate regime, limiting their adaptability and efficiency. This paper introduces deep variable-length feedback (DeepVLF) code, a novel DL-aided variable-length feedback coding scheme. By segmenting messages into multiple bit groups and employing a threshold-based decoding mechanism for independent decoding of each bit group across successive communication rounds, DeepVLF outperforms existing DL-based feedback codes and establishes a new benchmark in feedback channel coding.
ISSN:1938-1883
DOI:10.1109/ICC52391.2025.11160936