| 标 题： |
3D Positioning via Green Learning in mmWave Hybrid Beamforming Systems
基于毫米波混合波束成形系统的绿色学习3D定位 |
| 作 者： |
Kai-Rey Liu
Sau-Hsuan Wu
C. -C. Jay Kuo
Lie-Liang Yang
Kai-Ten Feng |
| 关键词： |
Green learning [绿色学习]
3D positioning [3D定位]
ISAC [ISAC]
NTN [NTN]
6G [6G] |
| 出版年： |
2024 |
| 文集名： |
99th IEEE Vehicular Technology Conference (VTC2024-Spring)
第99届IEEE车辆技术会议（VTC2024-春季） |
| 会议名： |
99th IEEE Vehicular Technology Conference (VTC2024-Spring)
第99届IEEE车辆技术会议（VTC2024-春季） |
| 届 次： |
99th |
| 摘 要： |
Three-dimensional (3D) positioning technology plays an important role in millimeter wave (mmWave) non-terrestrial or integrated sensing and communication (ISAC) networks in sixth-generation (6G) systems. However, the complexity of joint range and orientation estimation in mmWave hybrid beamforming (HBF) systems forms a technical hurdle to its practical realization. In view of the recent advancement in green learning (GL) technology, a low-complexity GL architecture is developed herein for 3D positioning in mmWave HBF systems. The entire architecture only consists of one layer of unsupervised representation learning, followed by a supervised feature learning stage and a regression layer for parameter estimation. Compared to the typical deep learning method, the complexity of the proposed method is at least 3 order lower, while the performance is comparable to that of the maximum likelihood estimations of individual parameters given perfect knowledge of the other parameters. This presents the potential of GL in ISAC for future 6G systems.
三维（3D）定位技术在第六代（6G）系统中的毫米波（mmWave）非地面或集成感知与通信（ISAC）网络中发挥着重要作用。然而，mmWave混合波束成形（HBF）系统中联合距离和方位估计的复杂性，已成为其实际实现的技术障碍。鉴于近期绿色学习（GL）技术的进步，本文提出了一种低复杂度的GL架构，用于mmWave HBF系统的三维定位。该架构仅包含一个无监督的表示学习层，随后是监督特征学习阶段以及用于参数估计的回归层。与典型的深度学习方法相比，所提方法的计算复杂度至少降低了三个数量级，同时性能可与在完全知道其他参数的情况下对各参数进行最大似然估计的结果相当。这表明GL在未来的6G系统中ISAC应用具有巨大潜力。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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