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BeamNet:在不完美的CSI条件下,用于ISAC系统的无监督光束成形

Helitha Nimnaka¹, Samiru Gayan¹, Ruhui Zhang²

¹Department of Electronic and Telecommunication Engineering, University of Moratuwa, Katubedda 10400, Sri Lanka.

Entropy (Basel, Switzerland)

|February 27, 2026

概括

本研究介绍了BeamNet,这是一种用于集成传感和通信 (ISAC) 束形的无监督深度学习方法. BeamNet有效地平衡了通信和传感速率,即使有不完美的通道信息.

科学领域:

电气工程电气工程
计算机科学计算机科学
信号处理信号处理

背景情况:

综合传感和通信 (ISAC) 系统融合了雷达传感和无线通信,以提高效率.
传输束成形对于优化双功能系统的性能至关重要.
现有的方法通常需要完美的通道状态信息 (CSI) 或复杂的优化解决方案.

研究的目的:

提出BeamNet,这是一个无监督的深度学习框架,用于在ISAC系统中传输光束成形.
为了使通讯速率 (CR) 和传感速率 (SR) 在一般色和不完美的CSI下能够联合优化.
为了学习CR-SR帕雷托边界,而不需要光束成型标签或嵌入式解决器.

主要方法:

开发了BeamNet,这是一个无监督的深度学习框架,将杂的通道估计映射到光束形成向量.
通过最大化CR和SR的加权总和来实现训练有素的BeamNet端到端.
在Raleigh,Nakagami-m和Rician色通道中评估性能,CSI质量不同.

主要成果:

在完美的CSI场景中,BeamNet准确地复制了分析帕雷托最佳解决方案.
描述了不同色参数的CR-SR权衡,并评估了分布不匹配的稳定性.
与封闭式光束造型器相比,在不完美的CSI下表现出优异的性能,可恢复估计错误造成的性能损失.

结论:

无监督学习为ISAC在色环境中的光束形成提供了灵活而强大的方法.
BeamNet有效地处理不完美的通道状态信息,为未来的无线网络提供了实用解决方案.
该框架有效地学习CR-SR权衡,在具有挑战性的条件下优于传统方法.

关键词:

纳卡加米 - - 我正在色.梁成型梁成型梁成型梁成型没有完美的CSI.综合传感和通信 (ISAC) 系统没有监督的深度学习.

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