5G大规模MIMO系统的深度学习驱动波束形成

Q4 Engineering

Journal of Telecommunications and Information Technology Pub Date : 2023-10-31 DOI:10.26636/jtit.2023.4.1332

Ridha Ilyas Bendjillali, Mohammed Sofiane Bendelhoum, Ali Abderrazak Tadjeddine, Miloud Kamline

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引用次数: 0

摘要

在本研究中，提出了一种基于resnest的深度学习方法，用于5G大规模多输入多输出(MIMO)系统的波束形成。基于resnest的深度学习方法被用来简化和优化波束形成过程，从而提高5G及以后通信网络的性能和效率。对波束形成能力的研究揭示了在最小化干扰的同时最大化信道容量的潜力，从而消除了传统方法的固有局限性。该模型对动态信道条件具有良好的适应性，在各种干扰情况下优于传统技术。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Deep Learning-Powered Beamforming for 5G Massive MIMO Systems

In this study, a ResNeSt-based deep learning approach to beamforming for 5G massive multiple-input multiple-output (MIMO) systems is presented. The ResNeSt-based deep learning method is harnessed to simplify and optimize the beamforming process, consequently improving performance and efficiency of 5G and beyond communication networks. A study of beamforming capabilities has revealed potential to maximize channel capacity while minimizing interference, thus eliminating inherent limitations of the traditional methods. The proposed model shows superior adaptability to dynamic channel conditions and outperforms traditional techniques across various interference scenarios.

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来源期刊

Journal of Telecommunications and Information Technology Engineering-Electrical and Electronic Engineering

CiteScore

1.20

自引率

0.00%

发文量