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In this paper, we focus on the demodulation/decoding of the complexmodulations/codes that approach the Shannon capacity. Theoretically, themaximum likelihood (ML) algorithm can achieve the optimal error performancewhereas it has $\mathcal{O}(2^k)$ demodulation/decoding complexity with $k$denoting the number of information bits. Recent progress in deep learningprovides a new direction to tackle the demodulation and the decoding. Thepurpose of this paper is to analyze the feasibility of the neural network todemodulate/decode the complex modulations/codes close to the Shannon capacityand characterize the error performance and the complexity of the neuralnetwork. Regarding the neural network demodulator, we use the golden anglemodulation (GAM), a promising modulation format that can offer the Shannoncapacity approaching performance, to evaluate the demodulator. It is observedthat the neural network demodulator can get a close performance to the ML-basedmethod while it suffers from the lower complexity order in the low-order GAM.Regarding the neural network decoder, we use the Gaussian codebook, achievingthe Shannon capacity, to evaluate the decoder. We also observe that the neuralnetwork decoder achieves the error performance close to the ML decoder with amuch lower complexity order in the small Gaussian codebook. Limited by thecurrent training resources, we cannot evaluate the performance of thehigh-order modulation and the long codeword. But, based on the results of thelow-order GAM and the small Gaussian codebook, we boldly give our conjecture:the neural network demodulator/decoder is a strong candidate approach fordemodulating/decoding the complex modulations/codes close to the Shannoncapacity owing to the error performance of the near-ML algorithm and the lowercomplexity.

Rethinking: Deep-learning-based Demodulation and Decoding