Abstract The main aim of massive multiuser multiple-input multiple-output (MU-MIMO) system is to improve the throughput and spectral efficiency in 5G wireless networks. The performance of MU-MIMO system is severely influenced by inter-antenna interference (IAI) and multiuser interference (MUI). The IAI occurs due to space limitations at each user terminal (UT) and the MUI is added when one UT is in the vicinity of another UT in the same cellular network. IAI can be mitigated through a precoding scheme such as singular value decomposition (SVD), and MUI is suppressed by an efficient multiuser detection (MUD) schemes. The maximum likelihood (ML) detector has optimal performance; however, it has a highly complex structure and involves the need of a large number of computations especially in massive structures. Thus, the neighborhood search-based algorithm such as likelihood ascent search (LAS) has been found to be a better alternative for mitigation of MUI as it results in near optimal performance with low complexity. Most of the recent papers are aimed at eliminating either MUI or IAI, whereas the proposed work presents joint SVD precoding and LAS MUD to mitigate both IAI and MUI. The proposed scheme can achieve a near-optimal performance with smaller number of matrix computations.

中文翻译：

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编码大规模 MU-MIMO 系统的似然上升搜索检测以减轻 IAI 和 MUI

摘要 大规模多用户多输入多输出（MU-MIMO）系统的主要目标是提高5G无线网络的吞吐量和频谱效率。MU-MIMO系统的性能受到天线间干扰（IAI）和多用户干扰（MUI）的严重影响。IAI 的发生是由于每个用户终端 (UT) 的空间限制，当一个 UT 位于同一蜂窝网络中的另一个 UT 附近时，会添加 MUI。IAI 可以通过诸如奇异值分解 (SVD) 之类的预编码方案来减轻，而 MUI 可以通过有效的多用户检测 (MUD) 方案来抑制。最大似然 (ML) 检测器具有最佳性能；然而，它具有高度复杂的结构，并且涉及大量计算的需要，尤其是在大规模结构中。因此，已发现基于邻域搜索的算法（例如似然上升搜索 (LAS)）是缓解 MUI 的更好替代方法，因为它可以以低复杂度获得接近最佳的性能。大多数最近的论文旨在消除 MUI 或 IAI，而拟议的工作提出了联合 SVD 预编码和 LAS MUD 以减轻 IAI 和 MUI。所提出的方案可以通过较少数量的矩阵计算实现接近最优的性能。