ABSTRACT

Low-complexity neighbourhood search algorithms for a massive multiple-input multiple-output (MIMO) wireless system has fascinated recent research attention. It performs iterative searches in a constrained maximum-likelihood (ML) space for the solution vector. However, they drive an inversion of large-dimensional matrices with an enormous amount of computations resulting in them becoming practically infeasible. It motivates for development of low-complexity matrix-inversion-free data detection algorithm that is proficient in achieving near-optimal performance in an unconstrained ML space. Using these concepts and the conjugate gradient (CG) approach, this article proposes the computationally efficient CG-based likelihood ascent search (CGLAS) detector. A CGLAS detection algorithm is proposed to achieve a fast update vector within unconstrained ML space in conjugate descent direction with few iterations. Simulation results demonstrate that this robust detection algorithm exerts more influence rather than other recent state-of-the-art detection algorithms that achieve much better performance for massive MIMO systems with superior running time efficiency.

摘要

用于大规模多输入多输出 (MIMO) 无线系统的低复杂度邻域搜索算法引起了最近的研究关注。它在受约束的最大似然 (ML) 空间中对解向量执行迭代搜索。然而，它们通过大量计算驱动大维矩阵的反演，导致它们实际上变得不可行。它激发了开发低复杂度无矩阵求逆数据检测算法的动机，该算法精通在不受约束的机器学习空间中实现近乎最优的性能。使用这些概念和共轭梯度 (CG) 方法，本文提出了计算效率高的基于 CG 的似然上升搜索 (CGLAS) 检测器。提出了一种 CGLAS 检测算法，以在无约束的 ML 空间内以很少的迭代实现共轭下降方向的快速更新向量。仿真结果表明，这种鲁棒的检测算法比其他最近的最先进的检测算法产生更大的影响，这些算法在大规模 MIMO 系统中实现了更好的性能，并具有出色的运行时间效率。