As one of the main optical wireless communication technologies, visible light communication (VLC) is considered a promising supplement in 5G/6G and the internet of things (IoT) networks. The multiple-input–multiple-output (MIMO) multiplexing scheme can significantly enhance a VLC system's performance. To handle the blind MIMO demultiplexing, geometric vector extraction (GVE) algorithms are used in a mixture space (MS), which has not been presented in the extant literature. In a 2×2 VLC MIMO scenario, the singular value decomposition of an arbitrary MIMO channel matrix is introduced to discuss the one-to-one relationship between channel-matrix elements and the outline-shape conversion starting from the transmitted rectangular-shaped points in the MS. By extracting the vector features of the adjacent sides of the received parallelogram-shaped constellation points, we can obtain the elements of an unknown channel matrix and recover the superposed signals transmitted by two light-emitting diodes (LED). To obtain a vector from these constellation points, clustering is required to obtain the scattered points' centroids in advance. To avoid falling into a local minimum, we present a clustering algorithm with an initial-position-expansion pre-processing stage to place the initial centroids with the greatest accumulated distance. To enhance the computational efficiency, we enhance the clustering algorithm via a single iteration to obtain the centroids, which we named the GVE-for-iteration-once (GVE–IO) algorithm. The enhanced algorithm has an equivalent or better MIMO demultiplexing performance and quick convergence performance compared to the fast independent component analysis (FastICA) algorithm. Moreover, the GVE–IO algorithm's computational complexity and measured running time is reduced to 29% and 23%, respectively, of the FastICA algorithm' time costs. Finally, the experimental investigation shows that the proposed scheme exhibits a significantly reduced computational complexity in all discussed voltage peak-to-peak (Vpp) cases of LED and a Q-value enhancement in two-thirds of the Vpp combination regions for 50 input points.

中文翻译：

---

可见光通信系统中基于几何矢量提取的低复杂度MIMO解复用方案

作为主要的光无线通信技术之一，可见光通信 (VLC) 被认为是 5G/6G 和物联网 (IoT) 网络中很有前景的补充。多输入多输出 (MIMO) 复用方案可以显着提高 VLC 系统的性能。为了处理盲 MIMO 解复用，在混合空间 (MS) 中使用了几何矢量提取 (GVE) 算法，这在现有文献中尚未出现。在 2×2 VLC MIMO 场景中，引入任意 MIMO 信道矩阵的奇异值分解，讨论信道矩阵元素与从传输的矩形点开始的轮廓形状转换之间的一对一关系女士。通过提取接收到的平行四边形星座点相邻边的矢量特征，我们可以获得未知信道矩阵的元素，并恢复两个发光二极管（LED）传输的叠加信号。为了从这些星座点中获得一个向量，需要事先通过聚类获得散点的质心。为了避免陷入局部最小值，我们提出了一种具有初始位置扩展预处理阶段的聚类算法，以放置具有最大累积距离的初始质心。为了提高计算效率，我们通过单次迭代增强聚类算法以获得质心，我们将其命名为 GVE-for-iteration-once (GVE-IO) 算法。与快速独立分量分析（FastICA）算法相比，增强算法具有同等或更好的MIMO解复用性能和快速收敛性能。此外，GVE-IO 算法的计算复杂度和测量的运行时间分别降低到 FastICA 算法时间成本的 29% 和 23%。最后，实验研究表明，所提出的方案在所有讨论的 LED 电压峰峰值 (Vpp) 情况下表现出显着降低的计算复杂度，并且在 50 个输入点的三分之二 Vpp 组合区域中 Q 值增强. 分别是 FastICA 算法的时间成本。最后，实验研究表明，所提出的方案在所有讨论的 LED 电压峰峰值 (Vpp) 情况下都表现出显着降低的计算复杂度，并且在 50 个输入点的三分之二 Vpp 组合区域中 Q 值增强. 分别是 FastICA 算法的时间成本。最后，实验研究表明，所提出的方案在所有讨论的 LED 电压峰峰值 (Vpp) 情况下表现出显着降低的计算复杂度，并且在 50 个输入点的三分之二 Vpp 组合区域中 Q 值增强.