学习去噪和解码：一种新的极化码残差神经网络解码器
Learning to Denoise and Decode: A Novel Residual Neural Network Decoder for Polar Codes
摘要：
极化码由于其容量可实现的特性而被采用作为第五代新无线电（5G NR）标准中的控制信道编码方案。传统的极化解码算法，如连续消除（SC），遭受高延迟问题，因为它们的顺序解码性质。神经网络译码器（NND）由于具有单点译码和并行计算的能力，被证明是极化码译码器的候选者。然而，NND算法的误比特率（BER）性能仍然不如SC算法。在本文中，我们提出了一个残留的神经网络解码器（RNND）的极化码。与以往直接使用神经网络对接收到的符号进行解码不同，该算法在NND之前引入了基于残差学习的去噪模块。提出的残差学习去噪器能够从接收信号中去除大量的噪声。数值结果表明，我们提出的RND优于传统的NND的BER性能相当的延迟下。
英文摘要：
Polar codes have been adopted as the control channel coding scheme in the fifth generation new radio (5G NR) standard due to its capacity-achievable property. Traditional polar decoding algorithms such as successive cancellation (SC) suffer from high latency problem because of their sequential decoding nature. Neural network decoder (NND) has been proved to be a candidate for polar decoder since it is capable of oneshot decoding and parallel computing. Whereas, the bit-errorrate (BER) performance of NND is still inferior to that of SC algorithm. In this paper, we propose a residual neural network decoder (RNND) for polar codes. Different from previous works which directly use neural network for decoding symbols received from the channel, the proposed RNND introduces a denoising module based on residual learning before NND. The proposed residual learning denoiser is able to remove remarkable amount of noise from received signals. Numerical results show that our proposed RNND outperforms traditional NND with regard to the BER performance under comparable latency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
