The application discloses a geometric
constellation shaping joint coding modulation method based on
deep learning, and belongs to the geometric
constellation shaping method. By constructing an end-to-
end system containing a
deep learning modulator and a demodulator, a log-likelihood ratio calibration module is integrated at the end of the demodulator to output
soft information matched with a decoder; a multi-objective joint
loss function containing a cross-entropy loss, a generalized
mutual information loss and a boundary constraint loss is determined; a phased training strategy of first freezing the calibration module for basic
constellation shaping and then jointly fine-tuning is adopted; the trained modulator is deployed at a sending end for geometric constellation shaping mapping, and the demodulator is deployed at a receiving end to output a calibrated log-likelihood
ratio sequence to the decoder. The application realizes deep
collaboration of coding and modulation, and improves transmission reliability and
spectral efficiency.