论文标题
用于大量MIMO检测的基质分解
Matrix Decomposition for Massive MIMO Detection
论文作者
论文摘要
大量多输入多输出（MIMO）是第五代（5G）通信系统的关键技术。 MIMO符号检测是庞大的MIMO基带接收器的计算最密集的任务之一。在本文中，我们分析了矩阵分解算法的大型MIMO系统，由于其数值稳定性和模块化设计，传统上用于小规模的MIMO检测。我们介绍了基于QR，Cholesky和LDL分解算法的线性检测机制的计算复杂性，用于不同的大型MIMO构型。我们将它们与最先进的基于反演的大量MIMO检测方法进行了比较。结果为系统和非常大规模集成（VLSI）设计师提供了重要的见解，以根据其要求选择适当的大规模MIMO检测算法。
Massive multiple-input multiple-output (MIMO) is a key technology for fifth generation (5G) communication system. MIMO symbol detection is one of the most computationally intensive tasks for a massive MIMO baseband receiver. In this paper, we analyze matrix decomposition algorithms for massive MIMO systems, which were traditionally used for small-scale MIMO detection due to their numerical stability and modular design. We present the computational complexity of linear detection mechanisms based on QR, Cholesky and LDL-decomposition algorithms for different massive MIMO configurations. We compare them with the state-of-art approximate inversion-based massive MIMO detection methods. The results provide important insights for system and very large-scale integration (VLSI) designers to select appropriate massive MIMO detection algorithms according to their requirement.
