论文标题
编码调制系统的概率和几何形状的联合学习
Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systems
论文作者
论文摘要
我们引入了可训练的编码调制方案，该方案可以通过概率塑形，几何形状，位标记和拆除特定的信道模型以及广泛的信噪比（SNRS）的概率形状，几何形状，位标记和拆除，从而实现与位的互信息（BMI）的联合优化。与概率振幅成型（PA）相比，所提出的方法不仅限于对称概率分布，因此可以针对任何频道模型进行优化，并且可以与任何代码速率$ k/m $，$ m $，每个通道使用的位数和$ k $ $ k $的整数在$ 1 $ 1 $ 1 $ 1 $ 1到$ M-1 $中的范围内使用。所提出的方案可以学习由SNR确定的星座几何形状和概率分布的连续性。此外，使用神经网络（NN）扩展了具有麦克斯韦 - 博尔茨曼（MB）的PAS架构，该神经网络（NN）控制了QAM的MB分布连续的MB稳定性，该神经网络（NN）控制了正交振幅调制（QAM）星座的MB成型。进行仿真以基准基准在加性白色高斯噪声（AWGN）和不匹配的瑞利块褪色（RBF）通道上提出的关节概率和几何形状方案的性能。
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.
