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A Learning-Based Approach to Address Complexity-Reliability Tradeoff in OS Decoders

arXiv 2103.03860 ↗First posted 5 Mar 2021cs.IT cs.LG math.ITcited by 0

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Abstract (arXiv)

In this paper, we study the tradeoffs between complexity and reliability for decoding large linear block codes. We show that using artificial neural networks to predict the required order of an ordered statistics based decoder helps in reducing the average complexity and hence the latency of the decoder. We numerically validate the approach through Monte Carlo simulations.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 14 references that are in the map. First posted on arXiv 5 Mar 2021.

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References · 5

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Learning to Decode Linear Codes Using Deep Learningcited by 60
  3. 2018Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approachcited by 34
  4. 2017Deep Learning for Frame Error Probability Prediction in BICM-OFDM Systemscited by 1
  5. 2019Low-Latency Communication with Computational Complexity Constraintscited by 1

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