Rethinking: Deep-learning-based Demodulation and Decoding
Rethinking: Deep-learning-based Demodulation and Decoding
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反思：基于深度学习的解调和解码
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Fanggang Wang
中科院分区：
文献类型：
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作者：
Boxiang He;Zitao Wu;Fanggang Wang
在本文中，我们专注于解调/解码的复杂的调制/码，接近香农容量。理论上，最大似然（ML）算法可以获得最佳的误码性能，但它的解调/译码复杂度为$mathcal{O}（2^k）$，其中$k$表示信息比特数。深度学习的最新进展为解决解调和解码提供了新的方向。本文的目的是分析神经网络解调/解码接近香农容量的复杂调制/编码的可行性，并表征错误性能和神经网络的复杂性。对于神经网络解调器，我们使用黄金角度调制（GAM），一个有前途的调制格式，可以提供香农容量接近的性能，以评估解调器。结果表明，神经网络解调器可以获得接近ML的方法的性能，但它遭受了较低的复杂度阶在低阶GAM。对于神经网络解码器，我们使用高斯码书，实现香农容量，以评估解码器。我们还观察到，神经网络解码器实现了接近ML解码器的错误性能，在小高斯码本中具有低得多的复杂度。由于目前训练资源的限制，我们无法评估高阶调制和长码字的性能。但是，基于低阶GAM和小高斯码本的结果，我们大胆地提出了我们的猜想：由于近ML算法的错误性能和较低的复杂度，神经网络解调器/解码器是解调/解码接近香农容量的复杂调制/码的强有力的候选方案。
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.