哈斯温网:一个基于Swin变压器的Denoising框架,对毫米波MIMO系统提供混合注意力
Xi Han1, Houya Tu1, Jiaxi Ying2
1School of Artificial Intelligence and Computer, North China University of Technology, Beijing 100144, China.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
深度学习框架HASwinNet通过在低信号条件和有限的飞行员条件下提高准确性来增强6G综合传感和通信 (ISAC) 的毫米波频道估计.
科学领域:
- 无线通信系统无线通信系统
- 信号处理 信号处理
- 机器学习用于通信.
背景情况:
- 毫米波 (mmWave) 大规模的MIMO对于6G集成传感和通信 (ISAC) 至关重要,使高容量的回程和环境传感成为可能.
- 在毫米波系统中,精确的通道估计是具有挑战性的,因为噪声敏感性,稀疏的多路径和有限的试点资源,特别是在低SNR.
研究的目的:
- 提出HASwinNet,这是一个深度学习框架,用于消除毫米波频道的噪音.
- 为了应对毫米波ISAC系统中在有限的试点资源和低SNR条件下准确的通道估计的挑战.
主要方法:
- 开发了HASwinNet,这是一个使用Swin Transformer编码器进行表示学习的深度学习框架.
- 合并了两个分支机构,用于稀疏的代币提取和使用DFT,SE和IDFT操作的角度域精炼.
- 引入了角域感知损失,以强化光谱一致性和保存多路径结构.
主要成果:
- 与CNN,LSTM和U-Net基线相比,HASwinNet显著提高了正常化平均平方误差 (NMSE) 和比特误差率 (BER).
- 该框架有效地利用了角稀疏性,在试点限制条件下保持了性能优势.
- 使用Saleh-Valenzuela (S-V) 通道模型展示了卓越的性能.
结论:
- 哈斯温网为实际的6G毫米波回程应用提供了一个可扩展的解决方案.
- 拟议的框架显示了在ISAC场景中准确的通道恢复的巨大潜力,有利于通信和传感功能.
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