论文标题
使用复发性神经网络的低密度均衡检查代码的设计设计
Design of Capacity-Approaching Low-Density Parity-Check Codes using Recurrent Neural Networks
论文作者
论文摘要
在本文中，我们使用复发性神经网络（RNN）对密度演化（DE）进行建模，以设计二进制擦除通道的二进制擦除通道的不规则不规则低密度平均检查（LDPC）代码。特别是，我们提出了一种确定LDPC代码结构的程度分布系数的方法。我们将我们的RNN结构称为神经密度演化（NDE），并通过最大程度地降低实施渐近最佳设计属性的损耗函数以及代码的所需结构特性，从而确定与最佳设计相对应的RNN的权重。这使LDPC设计过程高度可配置，因为可以通过修改损失功能来添加约束以满足应用程序的要求。为了训练RNN，我们生成与预期通道噪声相对应的数据。我们从理论上分析了NDE的复杂性和最佳性，并将其与采用差异进化的传统设计方法进行了比较。模拟表明，NDE在渐近性能和复杂性方面都在差异进化中得到改善。尽管我们专注于渐近设置，但我们评估了NDE发现的有限码编码长度的设计，并观察到在各种渠道中的性能仍然令人满意。
In this paper, we model Density Evolution (DE) using Recurrent Neural Networks (RNNs) with the aim of designing capacity-approaching Irregular Low-Density Parity-Check (LDPC) codes for binary erasure channels. In particular, we present a method for determining the coefficients of the degree distributions, characterizing the structure of an LDPC code. We refer to our RNN architecture as Neural Density Evolution (NDE) and determine the weights of the RNN that correspond to optimal designs by minimizing a loss function that enforces the properties of asymptotically optimal design, as well as the desired structural characteristics of the code. This renders the LDPC design process highly configurable, as constraints can be added to meet applications' requirements by means of modifying the loss function. In order to train the RNN, we generate data corresponding to the expected channel noise. We analyze the complexity and optimality of NDE theoretically, and compare it with traditional design methods that employ differential evolution. Simulations illustrate that NDE improves upon differential evolution both in terms of asymptotic performance and complexity. Although we focus on asymptotic settings, we evaluate designs found by NDE for finite codeword lengths and observe that performance remains satisfactory across a variety of channels.
