# Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions

arXiv: https://arxiv.org/abs/2009.07774 · HTML: https://mlsanity.com/paper/2009.07774/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Realizing Neural Decoder at the Edge with Ensembled BNN](https://mlsanity.com/paper/2106.09925/) (2021)
- [An Adaptive CSI Feedback Model Based on BiLSTM for Massive MIMO-OFDM Systems](https://mlsanity.com/paper/2408.06359/) (2024)

## Abstract (arXiv)

We consider the problem of recovering channel code parameters over a candidate set by merely analyzing the received encoded signals. We propose a deep learning-based solution that I) is capable of identifying the channel code parameters for any coding scheme (such as LDPC, Convolutional, Turbo, and Polar codes), II) is robust against channel impairments like multi-path fading, III) does not require any previous knowledge or estimation of channel state or signal-to-noise ratio (SNR), and IV) outperforms related works in terms of probability of detecting the correct code parameters.