论文标题
数据驱动的合奏，用于深度和硬性混合解码
Data-Driven Ensembles for Deep and Hard-Decision Hybrid Decoding
论文作者
论文摘要
集合模型被广泛用于通过分解为多个简单任务来求解复杂的任务，每个任务都由集合的一个成员在本地解决。由于维数的诅咒，对误差校正代码的解码是一个严重的问题，导致人们将审判器的组件视为可能的解决方案。但是，必须考虑复杂性，尤其是在解码方面。我们建议一种低复杂的方案，其中一个成员参与每个单词的解码。首先，可行单词的分布被分配到非重叠区域。此后，专业专家是通过独立培训单个地区的每个成员来形成的。使用经典的硬否定解码器（HDD）以注入式的方式将每个单词映射到单个专家。在瀑布区域的FER收益最高为0.4dB，在两个BCH（63,36）（63,36）和（63,45）代码中，具有循环降低的奇偶校验检查矩阵的代码可实现1.25dB，与先前的文章最佳结果相比，该代码为“线性代码的活跃深度解码”。
Ensemble models are widely used to solve complex tasks by their decomposition into multiple simpler tasks, each one solved locally by a single member of the ensemble. Decoding of error-correction codes is a hard problem due to the curse of dimensionality, leading one to consider ensembles-of-decoders as a possible solution. Nonetheless, one must take complexity into account, especially in decoding. We suggest a low-complexity scheme where a single member participates in the decoding of each word. First, the distribution of feasible words is partitioned into non-overlapping regions. Thereafter, specialized experts are formed by independently training each member on a single region. A classical hard-decision decoder (HDD) is employed to map every word to a single expert in an injective manner. FER gains of up to 0.4dB at the waterfall region, and of 1.25dB at the error floor region are achieved for two BCH(63,36) and (63,45) codes with cycle-reduced parity-check matrices, compared to the previous best result of the paper "Active Deep Decoding of Linear Codes".
