Efficient Training Scheme for Neural Network Based 4K-QAM Soft Demapper (Contemporary Mathematics, 2025)
The practicality of the densely packed and spectrally efficient 4096-Quadrature Amplitude Modulation (4K-QAM) is obstructed by the ultra-high computational complexity of its soft demapper, which is essential for generating the soft outputs required by channel decoders. In this paper, we propose an efficient training scheme to build a highly effective neural network based 4K-QAM soft demapper that can offer significantly lower computational complexity. The results demonstrate that this alternative demapper can achieve comparable decoding performance in coded 4K-QAM systems, while reducing computational complexity by up to 20% compared with the well-known low-complexity max-logarithm of maximum a posteriori (log-MAP) demapper.
Keywords: 4K-QAM; soft demapper; soft demodulator; soft bit; Log-Likelihood Ratio (LLR); machine learning; neural networks; Low-Density Parity-Check (LDPC) codes