谱卷积分解 Transformer（SCFT）用于联合多径参数估计：面向毫米波定位与无线电 SLAM
Spectral-Convolutional Factorized Transformer for Joint Multipath Parameter Estimation: Applications to mmWave Positioning and Radio SLAM
AI 导读
研究者提出谱卷积分解 Transformer（SCFT），从含噪信道估计和波束对描述中联合估计多径分量的时延、AoA 和 AoD，并检测视距/非视距路径。在真实射线追踪数据上，SCFT 相比最强基线 ResNet 将 GOSPA 误差降低 13.1%。在独立快照定位与无线电 SLAM 中，位置 RMSE 分别降低 18.5% 和 65.0%。
正文 · 原文
Abstract:Future 6G wireless networks are expected to evolve toward AI-native systems that support intelligent, environment-aware services, including positioning and mapping. Current research has already integrated AI into several aspects of communication, particularly wireless channel estimation. However, most learning-based channel estimators focus on communication-oriented objectives rather than explicitly estimating multipath parameters such as delay, angle of arrival (AoA), and angle of departure (AoD). These parameters provide essential geometric information for downstream tasks including positioning and radio simultaneous localization and mapping (SLAM). In this work, we propose a Spectral-Convolutional Factorized Transformer (SCFT) for joint multipath component (MPC) estimation from noisy complex channel estimates and physical beam-pair descriptors. SCFT combines local spectral convolution, permutation-equivariant beam-set attention, and a query-based Transformer decoder to detect and classify line-of-sight and non-line-of-sight paths and estimate their observed delays, AoAs, and AoDs. Experiments on realistic ray-tracing data show that SCFT reduces the generalized optimal sub-pattern assignment error by 13.1% compared with ResNet, the strongest competing baseline. We further demonstrate this advantage in independent snapshot positioning and radio SLAM, where SCFT reduces position RMSE by 18.5% and 65.0%, respectively, relative to ResNet. These findings highlight the importance of explicitly recovering multipath geometry, as improvements in MPC estimation can translate into substantial gains in downstream tasks.
| Subjects: | Signal Processing (eess.SP); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.01491 [eess.SP] |
| (or arXiv:2611.01491v2 [eess.SP] for this version) | |
| https://doi.org/10.48550/arXiv.2611.01491 arXiv-issued DOI via DataCite |
Submission history
From: Yaya Etiabi [view email]
[v1]
Sun, 1 Nov 2026 03:23:27 UTC (1,504 KB)
[v2]
Tue, 10 Nov 2026 05:28:19 UTC (1,466 KB)
来源：arXiv cs.AI · arxiv.org