在3GPP兼容的无线通道中用于Uplink MU-SIMO的无监督神经光束形成
Cemil Vahapoglu1,2, Timothy J O'Shea2, Wan Liu2
1Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
深度学习光束成型为无线系统提供了适应性解决方案. 基于变压器的模型在复杂的场景中表现出色,而更简单的神经网络则为不那么苛刻的条件提供了高效的替代方案,优于传统方法.
科学领域:
- 无线通信系统无线通信系统
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 传统的线性光束成型 (ZFBF,MMSE) 与不完美的CSI和高维度等非理想条件作斗争.
- 深度学习通过利用神经网络功能提供了适应性,数据驱动的束形解决方案.
- 现有的深度学习方法缺乏在现实的无线环境中进行系统的比较和分析.
研究的目的:
- 系统地比较两个无监督的深度学习波束成形架构用于上链接接收波束成形.
- 在多用户系统中对传统方法 (ZFBF,MMSE) 进行性能评估.
- 分析这些深度学习模型的计算复杂性和可扩展性.
主要方法:
- 开发并比较了一个简单的神经网络光束成形 (NNBF) 模型 (CNNs,完全连接的层) 和基于变压器的NNBF模型.
- 在使用3GPP TDL-A和UMa通道模型的多用户单输入多输出 (MU-SIMO) 系统中评估模型.
- 进行了基于FLOP的复杂性分析,以表征推理时间缩放行为.
主要成果:
- 基于变压器的NNBF在现实条件下 (不完善的CSI,移动性) 实现了卓越的性能,尽管计算成本更高.
- 简单的NNBF显示了比ZFBF/MMSE具有相似或更好的性能,在简化假设下 (完美的CSI,静态UE) 的复杂性明显较低.
- 这两种NNBF架构都显示出适应性和可扩展性束形解决方案的潜力.
结论:
- 深度学习光束成型,特别是基于变压器的模型,为传统的无线通信方法提供了强大而适应性的替代方案.
- 简单和基于变压器的NNBF之间的选择取决于性能要求和计算约束之间的权衡.
- 这项研究为深度学习在光束成形中的实际应用和性能扩展提供了宝贵的见解.
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