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带有巨大的基于MIMO的混合光束形成的光谱能量平衡系统,用于使用双重深度学习模型的无线6G通信

Ramesh Sundar¹, Mohammad Amir², Ranjith Subramanian³

¹Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology Kattankulathur, Chennai, India.

|February 23, 2024

概括

双深网络 (DDN) 混合波束成形方法通过减少开销来增强毫米波大规模MIMO系统. 这种新的方法提高了光谱效率和比特错误率 (BER) 的性能.

科学领域:

电气工程和计算机科学
无线通信系统无线通信系统
信号处理信号处理

背景情况:

毫米波 (mmWave) 大规模的MIMO系统在混合波束成形中面临着重大空中挑战.
现有的光束成形技术很难适应多样化和动态的通道状态.
有效的统计结构提取和网络培训对于强大的混合光束成形至关重要.

研究的目的:

通过使用双深网络 (DDN) 引入一种有效的混合波束成形方法,以减轻毫米波大规模MIMO系统的开销.
开发一个DDN技术来提取统计结构和训练网络地图函数用于混合光束成形.
评估使用拟议的基于DDN的混合架构的传输和接收实体的性能改进.

主要方法:

为混合光束成形提出了一种双深网络 (DDN) 模型.
通过使用各种通道变体和状态的通信数据序列来训练DDN.
性能通过分析比特错误率 (BER) 与信号噪声比率 (SNR) 和光谱效率与SNR进行评估,并与经典方法进行交叉验证.

主要成果:

拟议的基于DDN的混合光束成形方法在传输和接收方面都显示出显著的性能改进.
关键指标显示增强,达到7Kbits/s/Hz的光谱效率和1e-1的BER.
计算成本和性能估计得到了改进,有了增强的光束成形程序和多分辨率代码书指标.

结论:

双深网络方法有效地克服了毫米波大规模MIMO混合光束成形中的空头问题.
在多种道状态中,DDN提供了强大的性能,提高了光谱效率和BER.
拟议的方法为先进的无线通信系统提供了计算效率高和高性能解决方案.

关键词:

6G 通信通信 6G 通信模拟前编码器的模拟前编码器梁成型梁成型梁成型在 DDN DDN 的位置上.数字预编码器数字预编码器双重深度网络双重深度网络能量平衡是指能源的平衡.混合网络架构混合网络架构这就是"MIMO MIMO".毫米波的波浪.

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