论文标题
学会的神经信念传播解码器的驱逐
Learned Decimation for Neural Belief Propagation Decoders
论文作者
论文摘要
我们介绍了一个两阶段的分解过程，以提高Nachmani等人最近引入的神经信念传播（NBP）的性能，以供短期低密度平价检查（LDPC）代码。在第一阶段，我们通过在常规的NBP解码器和猜测最不可靠的位之间迭代来构建列表。第二阶段在常规的NBP解码器和学习的分解之间迭代，我们使用神经网络来确定每个位的分解值。对于（128,64）LDPC代码，提议的NBP耗竭的NBP优于NBP的NBP解码为0.75 dB，并且在1 dB中以最大可能的分解以$ 10^{-4} $的块错误解码执行。
We introduce a two-stage decimation process to improve the performance of neural belief propagation (NBP), recently introduced by Nachmani et al., for short low-density parity-check (LDPC) codes. In the first stage, we build a list by iterating between a conventional NBP decoder and guessing the least reliable bit. The second stage iterates between a conventional NBP decoder and learned decimation, where we use a neural network to decide the decimation value for each bit. For a (128,64) LDPC code, the proposed NBP with decimation outperforms NBP decoding by 0.75 dB and performs within 1 dB from maximum-likelihood decoding at a block error rate of $10^{-4}$.
