In this paper, we propose a low latency, robust and scalable neural net based decoder for convolutional and low-density parity-check (LPDC) coding schemes. The proposed decoders are demonstrated to have bit error rate (BER) and block error rate (BLER) performances at par with the state-of-the-art neural net based decoders while achieving more than 8 times higher decoding speed. The enhanced decoding speed is due to the use of convolutional neural network (CNN) as opposed to recurrent neural network (RNN) used in the best known neural net based decoders. This contradicts existing doctrine that only RNN based decoders can provide a performance close to the optimal ones. The key ingredient to our approach is a novel Mixed-SNR Independent Samples based Training (MIST), which allows for training of CNN with only 1\% of possible datawords, even for block length as high as 1000. The proposed decoder is robust as, once trained, the same decoder can be used for a wide range of SNR values. Finally, in the presence of channel outages, the proposed decoders outperform the best known decoders, {\it viz.} unquantized Viterbi decoder for convolutional code, and belief propagation for LDPC. This gives the CNN decoder a significant advantage in 5G millimeter wave systems, where channel outages are prevalent.
翻译:在本文中, 我们提出低潜值、 稳健且可扩缩的神经网的解码器, 用于 convolution 和低密度对等调( LPDC) 的编码方案。 拟议的解码器显示的比特错误率( BER) 和块误差率( BLWR) 的性能与最新神经网解码器的比特差率( MLULR) 相当, 并达到8倍以上的解码速度。 增强解码速度是由于使用 convolial 神经网( CNN) 而不是在已知的神经网( RNNNE) 的解码器中使用的经常性神经网( RNN) 。 这与现有的理论是矛盾的, 只有基于 RNNW 的解码器能提供接近最佳的性能。 我们的方法的关键要素是一个新的混合SNR独立样本( MIST) 培训, 它只允许对CNNC 进行1 ⁇ 可能的数据词的培训, 即使是1 000 的粗长。 。 。 拟议的解码是强大的, 一旦训练过的, 和同一解码的解码的解码系统可以用来在SNRLR 5( LNL) 的解码的系统中, 的解码中, 的解码解码系统中可以提供一个已知的大规模的解码。