论文标题
混合HMM解码器用于卷积代码，通过类似网格的结构和通道先验
Hybrid HMM Decoder For Convolutional Codes By Joint Trellis-Like Structure and Channel Prior
论文作者
论文摘要
无线链接的抗干扰能力是边缘计算的物理层问题。尽管卷积代码由于数据中引入的冗余而具有固有的误差校正潜力，但由于多径对通道的影响，卷积代码的性能大大退化。在本文中，我们建议使用隐藏的马尔可夫模型（HMM）重建卷积代码和Viterbi算法解码。此外，为了实施软性决策，HMM的观察被高斯混合模型（GMM）取代。我们的方法比标准方法提供了出色的误差校正潜力，因为模型参数包含通道状态信息（CSI）。我们评估了该方法的性能与数值模拟解码的标准Viterbi相比。在多径通道中，混合HMM解码器分别在使用硬否决和软否决解码时可以实现4.7 dB和2 dB的性能增益。 HMM解码器还为RSC代码实现了显着的性能增长，这表明该方法可以扩展到涡轮代码。
The anti-interference capability of wireless links is a physical layer problem for edge computing. Although convolutional codes have inherent error correction potential due to the redundancy introduced in the data, the performance of the convolutional code is drastically degraded due to multipath effects on the channel. In this paper, we propose the use of a Hidden Markov Model (HMM) for the reconstruction of convolutional codes and decoding by the Viterbi algorithm. Furthermore, to implement soft-decision decoding, the observation of HMM is replaced by Gaussian mixture models (GMM). Our method provides superior error correction potential than the standard method because the model parameters contain channel state information (CSI). We evaluated the performance of the method compared to standard Viterbi decoding by numerical simulation. In the multipath channel, the hybrid HMM decoder can achieve a performance gain of 4.7 dB and 2 dB when using hard-decision and soft-decision decoding, respectively. The HMM decoder also achieves significant performance gains for the RSC code, suggesting that the method could be extended to turbo codes.
