Joint Shaping of Geometry and Probability based on Mutual Information Neural Estimation
Jia-xi LIANG, Ze-kun NIU, Weisheng Hu, Lilin Yi · DOAJ (DOAJ: Directory of Open Access Journals) · 2022
In view of the problem that quadrature amplitude modulation has 1.53 dB capacity gap at the Shannon limit in high signal noise ratio, the paper proposes a joint shaping method of geometry and probability based on mutual information neural estimation. Geometric shaping and probability shaping are combined to improve the mutual information of the communication system. In this paper, the mutual information neural estimation is used to calculate the mutual information of the system, and the encoder of the transmitter is trained for the purpose of maximizing the mutual information, so as to realize geometric shaping and probability shaping. Through the simulation of Additive White Gaussian Noise (AWGN) channels with different signal noise ratios, it is proved that the performance of joint shaping of geometry and probability based on mutual information neural estimation is better than that of geometric shaping or probabilistic shaping separately. In AWGN channel with signal-to-noise ratio of 10 dB, the mutual information of the system has a gain of 0.041 7 bit/symbol over geometric shaping, and a gain of 0.027 9 bit/symbol compared over probability shaping.