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.

中文翻译：

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基于深度学习的 AWGN 和多径衰落条件下候选集信道码参数的盲识别

我们仅通过分析接收到的编码信号来考虑在候选集上恢复信道代码参数的问题。我们提出了一种基于深度学习的解决方案，I) 能够识别任何编码方案（例如 LDPC、卷积、Turbo 和 Polar 码）的信道码参数，II) 对信道损伤（如多径衰落）具有鲁棒性， III) 不需要任何先前的信道状态或信噪比 (SNR) 知识或估计，并且 IV) 在检测正确代码参数的概率方面优于相关工作。