论文标题
LDPC代码：使用顺序变异贝叶斯估计值跟踪非平稳通道噪声
LDPC codes: tracking non-stationary channel noise using sequential variational Bayesian estimates
论文作者
论文摘要
我们提出了一种使用概率图形模型在LDPC代码中跟踪非平稳信号噪声比率的顺序贝叶斯学习方法。我们使用通用群集图构造算法表示LDPC代码作为群集图，称为运行交叉属性（LTRIP）算法的分层树。通道噪声估计器是一个全局伽马簇，我们扩展到允许对非平稳噪声变化的贝叶斯跟踪。我们在现实世界5G驱动器测试数据上评估了我们提出的模型。我们的结果表明，我们的模型能够跟踪非平稳通道噪声，该噪声的表现超过了对实际通道噪声的固定知识的LDPC代码。
We present a sequential Bayesian learning method for tracking non-stationary signal-to-noise ratios in LDPC codes using probabilistic graphical models. We represent the LDPC code as a cluster graph using a general purpose cluster graph construction algorithm called the layered trees running intersection property (LTRIP) algorithm. The channel noise estimator is a global Gamma cluster, which we extend to allow for Bayesian tracking of non-stationary noise variation. We evaluate our proposed model on real-world 5G drive test data. Our results show that our model is capable of tracking non-stationary channel noise, which outperforms an LDPC code with a fixed knowledge of the actual average channel noise.
