基于互信息量估计的几何与概率联合整形技术

梁家熙; 牛泽坤; 胡卫生; 义理林

doi:10.13756/j.gtxyj.2022.03.001

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基于互信息量估计的几何与概率联合整形技术

光通信系统与网络技术 | 更新时间：2022-06-15

- 基于互信息量估计的几何与概率联合整形技术
- Joint Shaping of Geometry and Probability based on Mutual Information Neural Estimation
- 光通信研究 2022年第3期页码：1-6
- 作者机构：
  
  上海交通大学　区域光纤通信网与新型光通信系统国家重点实验室，上海　200240
- 作者简介：
  
  梁家熙(1997-)，男，吉林人。硕士，主要研究方向为光纤传输。
  义理林，教授。E-mail：lilinyi@sjtu.edu.cn
- 基金信息：
  
  科技部重点研发计划资助项目(2018YFB1800904)
- DOI：10.13756/j.gtxyj.2022.03.001
  中图分类号： TN919.3
- 纸质出版日期：2022-06-10，
  
  收稿日期：2021-12-13，
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梁家熙, 牛泽坤, 胡卫生, 等. 基于互信息量估计的几何与概率联合整形技术[J]. 光通信研究, 2022,(3):1-6.

LIANG Jia-xi, NIU Ze-kun, HU Wei-sheng, et al. Joint Shaping of Geometry and Probability based on Mutual Information Neural Estimation[J]. Study on Optical Communications, 2022,(3):1-6.
梁家熙, 牛泽坤, 胡卫生, 等. 基于互信息量估计的几何与概率联合整形技术[J]. 光通信研究, 2022,(3):1-6. DOI： 10.13756/j.gtxyj.2022.03.001.

LIANG Jia-xi, NIU Ze-kun, HU Wei-sheng, et al. Joint Shaping of Geometry and Probability based on Mutual Information Neural Estimation[J]. Study on Optical Communications, 2022,(3):1-6. DOI： 10.13756/j.gtxyj.2022.03.001.

摘要

针对正交振幅调制在高信噪比的加性高斯白噪声(AWGN)信道与香农极限有1.53 dB容量间隙的问题，文章提出了一种基于互信息量估计的几何与概率联合整形的方法，将几何整形与概率整形相结合，提升通信系统的互信息量。文章将互信息量估计作为计算系统互信息量的方式，以最大化互信息量为目的训练发端的编码器，实现几何整形与概率整形。通过在不同信噪比下AWGN信道中的仿真，验证了基于互信息量估计的几何与概率联合整形系统的性能要优于单独进行几何整形或概率整形的性能。在信噪比为10 dB的AWGN信道中，系统的互信息量与几何整形相比有0.041 7 bit/symbol的增益，与概率整形相比有0.027 9 bit/symbol的增益。

Abstract

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.

关键词

深度学习互信息量估计物理层通信

Keywords

deep learningmutual information neural estimationphysical layer communication

references

Chen B, Okonkwo C, Hafermann H, et al. Increasing Achievable Information Rates via Geometric Shaping[C]//2018 European Conference on Optical Communication (ECOC). Rome, Italy: IEEE, 2018: 1-3.

Zhang S, Yaman F, Mateo E, et al. A Generalized Pairwise Optimization for Designing Multi-Dimensional Modulation Formats[C]//Optical Fiber Communications Conference & Exhibition. Los Angeles, CA, USA: IEEE, 2017: 1-3.

Kschischang F R, Pasupathy S. Optimal Nonuniform Signaling for Gaussian Channels[J]. IEEE Transactions on Information Theory, 2002, 39(3): 913-929.

O’shea T, Hoydis J. An Introduction to Deep Learning for the Physical Layer[J]. IEEE Transactions on Cognitive Communications & Networking, 2017, 3(4): 563-575.

Goldsmith A. Joint Source/Channel Coding for Wireless Channels[C]//Vehicular Technology Conference, 1995 IEEE 45th. Chicago, IL, USA: IEEE, 1995: 614-618.

Stark M, Aoudia F A, Hoydis J. Joint Learning of Geometric and Probabilistic Constellation Shaping[C]//2019 IEEE Globecom Workshops (GC Wkshps). Waikoloa, HI, USA: IEEE, 2019: 1-6.

Belghazi M I, Baratin A, Rajeswar S, et al. MINE: Mutual Information Neural Estimation[J]. arXiv preprint arXiv: 1801.04062, 2018.

Paninski L. Estimation of Entropy and Mutual Information[J]. Neural Computation, 2014, 15(6): 1191-1253.

Donsker M, Varadhan S. Asymptotic Evaluation of Certain Markov Process for Large Time[J]. Communications on Pure and Applied Mathematics, 1975, 36(2): 183-212.

Jang E, Gu S, Poole B. Categorical Reparameterization with Gumbel-Softmax[J]. arXiv preprint arXiv: 1611.01144, 2016.

Fehenberger T, Alvarado A, Böcherer G, et al. On Probabilistic Shaping of Quadrature Amplitude Modulation for the Nonlinear Fiber Channel[J]. Journal of Lightwave Technology, 2016, 34(21): 5063-5073.

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