Evaluation of massive multiple-input multiple-output communication performance under a proposed improved minimum mean squared error precoding

D. Kadhim, M. Saleh, S. Abou-Loukh
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引用次数: 1

Abstract

The fundamental of a downlink massive multiple-input multiple-output (MIMO) energy- issue efficiency strategy is known as minimum mean squared error (MMSE) implementation degrades the performance of a downlink massive MIMO energy-efficiency scheme, so some improvements are adding for this precoding scheme to improve its workthat is called our proposal solution as a proposed improved MMSE precoder (PIMP). The energy efficiency (EE) study has also taken into mind drastically lowering radiated power while maintaining high throughput and minimizing interference issues. We further find the tradeoff between spectral efficiency (SE) and EE although they coincide at the beginning but later their interests become conflicting and divergent then leading EE to decrease so gradually while SE continues increasing logarithmically. The results achieved that for a single-cellular massive MU-MIMO downlink model, our PIMP scheme is the appropriate scenario to achieve higher precoding performance system. Furthermore, both maximum ratio transmission (MRT) and PIMP are suitable for performance improvement in massive MIMO results of EE and SE. So, the main contribution comes with this work that highest EE and SE are belong to use a PIMP which performs better appreciably than MRT at bigger ratio of number of antennas to the number of the users. 
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基于改进的最小均方误差预编码的海量多输入多输出通信性能评价
下行链路大规模多输入多输出(MIMO)节能策略的基础被称为最小均方误差(MMSE)实现,降低了下行链路大规模MIMO节能方案的性能,因此对该预编码方案进行了一些改进以改善其工作,我们的建议解决方案被称为改进的MMSE预编码器(PIMP)。能源效率(EE)研究也考虑到在保持高吞吐量和最小化干扰问题的同时大幅降低辐射功率。我们进一步发现频谱效率(SE)和EE之间的权衡,虽然它们在开始时是一致的,但后来它们的利益变得冲突和分歧,导致EE逐渐下降,而SE继续以对数增长。结果表明,对于单蜂窝大规模MU-MIMO下行链路模型,我们的PIMP方案是实现更高预编码性能的系统的合适方案。此外,最大比率传输(MRT)和PIMP都适用于EE和SE大规模MIMO结果的性能改进。因此,这项工作的主要贡献在于,最高的EE和SE属于使用PIMP,在天线数量与用户数量的较大比例下,PIMP的性能明显优于MRT。
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来源期刊
IAES International Journal of Artificial Intelligence
IAES International Journal of Artificial Intelligence Decision Sciences-Information Systems and Management
CiteScore
3.90
自引率
0.00%
发文量
170
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