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DGD-CNet:对美国国税局资助的大规模MIMO系统的CSI网络进行拒绝,使用一个基于脱落的CSI网络来拒绝封闭的定期单元

Amina Abdelmaksoud^1,2, Bassant Abdelhamid², Hesham Elbadawy³

¹Electronics and Communications Department, Faculty of Engineering, Modern Academy for Engineering and Technology, Cairo 11585, Egypt.

Sensors (Basel, Switzerland)

|September 28, 2024

概括

一个新的Denoising Gated Recurrent Unit与基于丢失的通道状态信息网络 (DGD-CNet) 改善了6G网络的通道估计. 这种人工智能模型减少了智能反射表面辅助大规模MIMO系统的反开销.

科学领域:

无线通信是一种无线通信.
电信领域的人工智能
信号处理信号处理

背景情况:

大规模的MIMO和智能反射表面 (IRS) 对6G网络至关重要,特别是在非视线 (NLoS) 条件下.
由于高反开销,被动IRS部署在基于频率分割双重 (FDD) 的大规模MIMO中面临道估计挑战.
现有的方法难以在复杂的无线环境中平衡反减少和准确性.

研究的目的:

引入一种新的深度学习模型,Denoising Gated Recurrent Unit with Dropout-based Channel state information Network (DGD-CNet),以实现高效的道估计.
为解决基于FDD的IRS辅助大规模MIMO系统中的反开销挑战.
为了提高频道估计的准确性,并捕捉时间变化频道的时空动态.

主要方法:

发展DGD-CNet模型,将Gated Recurrent Unit (GRU) 与 Dropout (DO) 整合起来,以加强学习.
将DGD-CNet模型应用于基于FDD的IRS辅助的大规模MIMO系统.
通过规范平均平方误差 (NMSE),相关系数和系统准确度指标进行性能评估.

主要成果:

DGD-CNet模型比现有方法取得了显著的改进,至少减少了26%的NMSE.
在室内环境下,在低压缩比 (Low-CR) 下,相关系数增加2%和系统精度增加4%.
该模型在各种压缩比和户外场景中表现出强大的性能.

结论:

拟议的 DGD-CNet 模型有效地减少了反开销,并提高了 6G IRS 辅助的大规模 MIMO 系统中通道估计的准确性.
集成GRU和DO使模型能够捕捉复杂的通道特征.
在未来的无线网络中,DGD-CNet为高效准确的通道估计提供了一个有希望的解决方案.

关键词:

在CSI的反中,CSI的反是非常重要的.总监局 - 网络总局拒绝封闭的经常性单位.FDD FDD FDD FDD 的意思是什么意思美国国税局 IRS IRS.大规模的MIMO.这是NMSE的NMSE.频道估计频道估计深度学习是一种深度学习.脱落技术的脱落技术是什么系统准确度系统准确度

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