基于RNN的混合预编码的光谱效率和BER分析,用于在太赫兹通信下进行无细胞大规模MIMO
Tadele A Abose1, Binyam G Assefa2, Yitbarek A Mekonen2
1Department of Electrical and Computer Engineering, Mattu University, Mattu, Ethiopia.
PloS one
|August 14, 2025
概括
循环神经网络 (RNN) 通过预测前编码权重以更好地估计通道来增强Terahertz (THz) 无细胞大规模MIMO系统. 这种先进的技术可以提高数据传输速度,并减少未来无线网络的错误.
科学领域:
- 无线通信工程 无线通信工程
- 信号处理 信号处理
- 机器学习在电信中的应用.
背景情况:
- 太赫兹 (THz) 通信和无细胞大规模多输入多输出 (CFMM) 系统为未来的无线网络提供高数据速率和低延迟.
- THz通信面临着诸如高路径损失和稀疏散射等挑战,复杂化了频道估计.
- 准确的通道估计对于优化CFMM系统中的预编码至关重要.
研究的目的:
- 在THz CFMM系统中研究基于循环神经网络 (RNN) 的混合预编码的有效性.
- 开发一种方法,共同设计用于动态通道条件和用户移动性的模拟和数字前编码器.
- 为了提高通道估计的准确性,并减轻在THz通信中的飞行员污染.
主要方法:
- 使用循环神经网络 (RNN) 来预测最佳的混合预编码权重.
- 训练RNN学习适应性预编码的空间和时间通道模式.
- 模拟和数字前编码器的联合设计,以适应动态无线环境.
主要成果:
- 基于RNN的混合预编码显著提高了光谱效率,在30dB SNR下达到10bps/Hz,而MMSE预编码为8.2bps/Hz.
- 拟议的方法实现了较低的比特误差率 (BER),达到10−6在11 dBSNR16-QAM,而MMSE需要12.5dB.
- 在各种信号噪声比 (SNR),天线配置和用户密度方面观察到一致的性能增长.
结论:
- 基于RNN的混合预编码是THz CFMM系统的高效技术.
- 该方法在光谱效率和BER方面表现出比传统技术更好的性能.
- 这种方法显示出在推进下一代无线通信系统方面的巨大潜力.
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