用神经网络降低删除信道极化码的解码复杂度，支持长码长应用。
Neural Polar Decoders for Deletion Channels
- 设计四神经网络结构，仅调整一个模块适配删除信道。
- 复杂度降至O(AN log N)，A为用户设定的计算预算。
- 可集成列表解码，适合DNA存储等未来技术场景。
本文提出一种用于恒定删除率删除信道的神经极化解码器（NPD）。现有极化码解码器在删除信道下复杂度高达O(N⁴)，限制了其在长码长下的应用。本文将NPD架构扩展至删除信道，仅修改其中一个神经网络模块以适配删除操作。新架构复杂度为O(AN log N)，其中参数A为用户设定的计算预算，与信道无关。在删除率δ∈{0.01, 0.1}条件下，通过Tal等人提出的网格解码器作为真实标签验证性能。结果表明，由于复杂度显著降低，可引入列表解码进一步提升性能。该方法有望应用于未来如DNA存储等高可靠性场景。
原文摘要 · Abstract (English)
This paper introduces a neural polar decoder (NPD) for deletion channels with a constant deletion rate. Existing polar decoders for deletion channels exhibit high computational complexity of $O(N^4)$, where $N$ is the block length. This limits the application of polar codes for deletion channels to short-to-moderate block lengths. In this work, we demonstrate that employing NPDs for deletion channels can reduce the computational complexity. First, we extend the architecture of the NPD to support deletion channels. Specifically, the NPD architecture consists of four neural networks (NNs), each replicating fundamental successive cancellation (SC) decoder operations. To support deletion channels, we change the architecture of only one. The computational complexity of the NPD is $O(AN\log N)$, where the parameter $A$ represents a computational budget determined by the user and is independent of the channel. We evaluate the new extended NPD for deletion channels with deletion rates $δ\in\{0.01, 0.1\}$ and we verify the NPD with the ground truth given by the trellis decoder by Tal et al. We further show that due to the reduced complexity of the NPD, we are able to incorporate list decoding and further improve performance. We believe that the extended NPD presented here could have applications in future technologies like DNA storage.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。