arXiv：cs.LG· Anjali K. S., Manoj Kumar Panda, Chandramani Singh·· 3 小时前
ISAC 实时波束成形中深度学习 DoA 估计器的对比评估
Comparative Evaluation of Deep-Learning based DoA Estimators for Real-Time ISAC Beamforming
AI 导读
研究构建统一评估框架，在 ISAC 波束成形流水线中对比经典与深度学习 DoA 估计器及一种集成架构，并与基于 SVD 的波束成形在相同条件下联合评测。结果显示学习类方法较 MUSIC 大幅缩短估计时间，但整体处理时延 increasingly 由 SVD 波束成形的计算开销主导；其中回归卷积神经网络（RCNN）在估计精度、计算效率与波束成形性能间取得较好平衡。
正文
Abstract:Integrated Sensing and Communication (ISAC) systems rely on accurate Direction-of-Arrival (DoA) estimation for effective beamforming and spectral-efficiency enhancement. While numerous classical and deep-learning-based DoA estimation methods have been reported in the literature, their suitability for real-time ISAC operation remains insufficiently understood because practical deployment requires consideration of both estimation accuracy and computational latency. This paper presents a unified evaluation framework for systematically comparing classical and learning-based DoA estimators within an ISAC beamforming pipeline. Representative classical and learning-based DoA estimators, together with an ensemble architecture, are integrated with Singular Value Decomposition (SVD)-based beamforming and evaluated under identical operating conditions. The impact of DoA estimation accuracy, inference latency, beamforming-weight generation time, and resulting effective spectral efficiency is investigated. Results show that different estimators occupy distinct operating points in the accuracy-latency-performance design space. Although learning-based approaches substantially reduce estimation time compared with MUSIC, the overall processing latency increasingly becomes dominated by the computational cost of SVD-based beamforming. Among the evaluated methods, the Regression Convolutional Neural Network (RCNN) exhibits a favorable balance between estimation accuracy, computational efficiency, and beamforming performance, making it a promising candidate for real-time ISAC deployment. The findings further suggest that future low-latency ISAC systems may benefit from beamforming strategies that reduce or avoid computationally intensive SVD-based beamforming-weight generation.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.20326 [cs.LG] |
| (or arXiv:2610.20326v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20326 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Manoj Kumar Panda [view email]
[v1]
Wed, 14 Oct 2026 15:11:29 UTC (265 KB)
来源：arXiv：cs.LG · arxiv.org