论文标题
基于深度学习的盲目识别对AWGN和多路径褪色条件下的候选人集的通道代码参数
Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions
论文作者
论文摘要
我们考虑通过仅分析接收到的编码信号来恢复候选者的频道代码参数的问题。我们提出了一种基于学习的深度解决方案，i）能够识别任何编码方案（例如LDPC，卷积，卷积，涡轮和极地代码）的通道代码参数，ii）ii）与诸如多路径褪色（III）（III）（III）（III）等通道障碍的鲁棒性不需要任何以前的频道状态或信号级别的概述（SNR）的知识（SNR），以及IV的概述（IV），以及IV的概念。 参数。
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.
