In wireless communication systems, there are many stages for signal transmission. Among them, mapping and demapping convert a sequence of bits into a sequence of complex numbers and vice versa. This operation is performed by a system of constellations~ -- by a set of labeled points on the complex plane. Usually, the geometry of the constellation is fixed, and constellation points are uniformly spaced, e.g., the same quadrature amplitude modulation (QAM) is used in a wide range of signal-to-noise ratio (SNR). By eliminating the uniformity of constellations, it is possible to achieve greater values of capacity. Due to the current standard restrictions, it is difficult to change the constellation both on the mapper or demapper side. In this case, one can optimize the constellation only on the mapper or the demapper side using original methodology. By the numerical calculating of capacity, we show that the optimal geometric constellation depends on SNR. Optimization is carried out by maximizing mutual information (MI). The MI function describes the amount of information being transmitted through the channel with the optimal encoding. To prove the effectiveness of this approach we provide numerical experiments in the modern physical level Sionna simulator using the realistic LDPC codes and the MIMO 5G OFDM channels.

翻译：在无线通信系统中，信号传输包含多个处理环节。其中，映射与解映射操作实现比特序列与复数序列之间的相互转换，该过程通过星座系统（即复平面上带有标签的点的集合）完成。通常，星座的几何结构是固定的，且星座点呈均匀分布，例如在广泛的信噪比（SNR）范围内使用相同的正交幅度调制（QAM）。通过消除星座的均匀性，可以实现更高的信道容量。受当前标准限制，在映射端或解映射端均难以改变星座结构。在此情况下，可采用原始方法仅在映射端或解映射侧对星座进行优化。通过数值计算信道容量，我们证明最优几何星座依赖于SNR。优化过程通过最大化互信息（MI）实现，MI函数描述了在最优编码条件下通过信道传输的信息量。为验证该方法的有效性，我们在现代物理层仿真器Sionna中，采用实用LDPC码与MIMO 5G OFDM信道开展了数值实验。