论文标题
重新思考：基于深度学习的解调和解码
Rethinking: Deep-learning-based Demodulation and Decoding
论文作者
论文摘要
在本文中，我们专注于接近香农容量的复杂调制/代码的解调/解码。从理论上讲，最大可能性（ML）算法可以实现最佳错误性能，而它具有$ \ MATHCAL {O}（2^k）$ DEDODUTION/DEDODUTION/DEDODUTION/用$ K $表示信息位的数量。深度学习的最新进展为应对解调和解码的新方向提供了新的方向。本文的目的是分析神经网络的可行性来解调/解码接近香农容量的复杂调制/代码，并表征神经网络的误差性能和复杂性。关于神经网络解调剂，我们使用Golden Angle调制（GAM），这是一种有希望的调制格式，可以提供Shannon容量接近性能，以评估解调器。据观察，神经网络解调器可以与基于ML的方法保持紧密的性能，而低阶GAM中的复杂性顺序较低。关于神经网络解码器，我们使用高斯密码手册，达到香农容量来评估解码器。我们还观察到，神经网络解码器在小高斯密码簿中以较低的复杂性顺序达到了靠近ML解码器的错误性能。受当前培训资源的限制，我们无法评估高级调制和长密码字的性能。但是，根据低阶GAM和小型高斯密码手册的结果，我们大胆地给出了猜想：神经网络解调器/解码器是一种强大的候选方法，用于解码/解码靠近Shannon容量的复杂调制/代码，这是由于近乎ML AlgorithM和较低的复杂性的错误性能。
In this paper, we focus on the demodulation/decoding of the complex modulations/codes that approach the Shannon capacity. Theoretically, the maximum likelihood (ML) algorithm can achieve the optimal error performance whereas it has $\mathcal{O}(2^k)$ demodulation/decoding complexity with $k$ denoting the number of information bits. Recent progress in deep learning provides a new direction to tackle the demodulation and the decoding. The purpose of this paper is to analyze the feasibility of the neural network to demodulate/decode the complex modulations/codes close to the Shannon capacity and characterize the error performance and the complexity of the neural network. Regarding the neural network demodulator, we use the golden angle modulation (GAM), a promising modulation format that can offer the Shannon capacity approaching performance, to evaluate the demodulator. It is observed that the neural network demodulator can get a close performance to the ML-based method while it suffers from the lower complexity order in the low-order GAM. Regarding the neural network decoder, we use the Gaussian codebook, achieving the Shannon capacity, to evaluate the decoder. We also observe that the neural network decoder achieves the error performance close to the ML decoder with a much lower complexity order in the small Gaussian codebook. Limited by the current training resources, we cannot evaluate the performance of the high-order modulation and the long codeword. But, based on the results of the low-order GAM and the small Gaussian codebook, we boldly give our conjecture: the neural network demodulator/decoder is a strong candidate approach for demodulating/decoding the complex modulations/codes close to the Shannon capacity owing to the error performance of the near-ML algorithm and the lower complexity.
