论文标题
解释神经微米解码器
Interpreting Neural Min-Sum Decoders
论文作者
论文摘要
在解码线性块代码时，可以通过向信念传播（BP）解码器引入可学习的参数来实现明显的可靠性增长。尽管这些方法取得了成功，但仍有两个关键的开放问题。首先是缺乏对学习权重的解释，另一个是缺乏对非AWGN通道的分析。在这项工作中，我们的目标是通过提供对所学性的权重及其与基础代码结构的联系来弥合这一差距。我们表明，重量受到代码中短周期的分布的很大影响。接下来，我们将研究这些解码器在合成和空中渠道的非AWGN通道中的性能，并研究复杂性与性能权衡，这表明增加参数的数量在复杂的通道中有助于显着。最后，我们表明，具有学习权重的解码器的可靠性比在高斯近似下分析优化的权重优化的解码器具有更高的可靠性。
In decoding linear block codes, it was shown that noticeable reliability gains can be achieved by introducing learnable parameters to the Belief Propagation (BP) decoder. Despite the success of these methods, there are two key open problems. The first is the lack of interpretation of the learned weights, and the other is the lack of analysis for non-AWGN channels. In this work, we aim to bridge this gap by providing insights into the weights learned and their connection to the structure of the underlying code. We show that the weights are heavily influenced by the distribution of short cycles in the code. We next look at the performance of these decoders in non-AWGN channels, both synthetic and over-the-air channels, and study the complexity vs. performance trade-offs, demonstrating that increasing the number of parameters helps significantly in complex channels. Finally, we show that the decoders with learned weights achieve higher reliability than those with weights optimized analytically under the Gaussian approximation.
