Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systems
Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systems
复制标题
编码调制系统的概率和几何整形的联合学习
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
J. Hoydis
中科院分区：
文献类型：
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作者：
Fayçal Ait Aoudia;J. Hoydis
我们引入了一种可训练的编码调制方案，该方案通过概率整形、几何整形、位标记和针对特定信道模型和大范围信噪比（SNRs）的解映射来联合优化比特互信息（BMI）。与概率幅度整形（PAS）相比，所提出的方法不限于对称概率分布，可以针对任何信道模型进行优化，并且可以在任何码率$k/m下工作，m$是每个信道使用的位数，而$k$是1到$m-1$范围内的整数。所提出的方案能够学习由信噪比决定的星座几何和概率分布的连续体。此外，利用神经网络（NN）扩展了以麦克斯韦-玻尔兹曼（MB）为整形分布的PAS结构，该神经网络根据信噪比控制正交调幅（QAM）星座的MB整形，使QAM的MB分布连续体学习成为可能。仿真测试了所提出的联合概率和几何整形方案在加性高斯白噪声（AWGN）和不匹配瑞利块衰落（RBF）信道下的性能。
We introduce a trainable coded modulation scheme that enables joint optimization of the bit-wise mutual information (BMI) through probabilistic shaping, geometric shaping, bit labeling, and demapping for a specific channel model and for a wide range of signal-to-noise ratios (SNRs). Compared to probabilistic amplitude shaping (PAS), the proposed approach is not restricted to symmetric probability distributions, can be optimized for any channel model, and works with any code rate $k/m, m$ being the number of bits per channel use and $k$ an integer within the range from 1 to $m-1$. The proposed scheme enables learning of a continuum of constellation geometries and probability distributions determined by the SNR. Additionally, the PAS architecture with Maxwell-Boltzmann (MB) as shaping distribution was extended with a neural network (NN) that controls the MB shaping of a quadrature amplitude modulation (QAM) constellation according to the SNR, enabling learning of a continuum of MB distributions for QAM. Simulations were performed to benchmark the performance of the proposed joint probabilistic and geometric shaping scheme on additive white Gaussian noise (AWGN) and mismatched Rayleigh block fading (RBF) channels.