Multiple-access interference suppression in massive multiple-input multiple-output-non-orthogonal multiple access based on space–time block coding,International Journal of Communication Systems

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Multiple-access interference suppression in massive multiple-input multiple-output-non-orthogonal multiple access based on space–time block coding
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2021-05-14 , DOI: 10.1002/dac.4824
Behnam Akbarian ₁ , Saeed Ghazi‐Maghrebi ₁

Affiliation

Non-orthogonal multiple-access (NOMA) scheme using massive multiple-input multiple-output (MIMO) transmission suffers from interference between users. In this paper, we propose a novel MIMO-NOMA framework based on space–time block code (STBC) to decrease the multiple-access interference (MAI), when the number of users is increasing. In this paper, besides giving an analysis of network, cluster, group, and users, we first present the system model. Then, an efficient precoder and detector are designed based on interference analysis. In particular, the proposed scheme can decompose a massive MIMO-NOMA system into multiple separated single-input single-output NOMA channels. Also, analytical results are developed to evaluate the performance of the system. To address the dramatic increment of power level, with the aid of the proposed algorithm, the optimal solution is achieved. Mathematical analyses for both MIMO-NOMA and STBC are presented to design efficient precoding and detection. Finally, simulation results, based on STBC, are provided to verify the theoretical analysis to demonstrate significant capacity improvements of the massive MIMO-NOMA.

中文翻译：

基于空时分组编码的海量多输入多输出非正交多址多址干扰抑制

使用大规模多输入多输出 (MIMO) 传输的非正交多址 (NOMA) 方案会受到用户之间的干扰。在本文中，我们提出了一种基于空时分组码 (STBC) 的新型 MIMO-NOMA 框架，以在用户数量增加时减少多址干扰 (MAI)。在本文中，除了对网络、集群、组和用户进行分析外，我们首先介绍了系统模型。然后，基于干扰分析设计了高效的预编码器和检测器。特别是，所提出的方案可以将大规模 MIMO-NOMA 系统分解为多个分离的单输入单输出 NOMA 通道。此外，还开发了分析结果以评估系统的性能。为了解决功率电平的急剧增加，借助所提出的算法，达到了最优解。提出了 MIMO-NOMA 和 STBC 的数学分析，以设计有效的预编码和检测。最后，提供了基于 STBC 的仿真结果来验证理论分析，以证明大规模 MIMO-NOMA 的显着容量改进。

更新日期：2021-06-21

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