arXiv cs.AI· Nhan Thanh Nguyen, Phuong Nam Tran, Mengyuan Ma, Huy T. Nguyen, Ahmed Alkhateeb, Markku Juntti·· 6 天前AI 评分18
节能脉冲神经网络用于感知辅助波束预测
Energy-Efficient Spiking Neural Networks for Sensing-Aided Beam Prediction
AI 导读
研究针对毫米波系统中感知辅助波束预测的高能耗问题，开发了脉冲神经网络（SNN）架构，并与传统人工神经网络（ANN）在位置、视觉及多模态输入下系统对比。
正文 · 原文
Abstract:Sensing-aided beam prediction can reduce the beam-training overhead of millimeter-wave (mmWave) systems by leveraging environmental information from complementary modalities such as vision and position. However, the resulting sensing and inference pipeline can be energy-intensive, motivating the use of energy-efficient spiking neural networks (SNNs) as an alternative to conventional artificial neural networks (ANNs). In this work, we develop SNN architectures for beam prediction and systematically compare them with ANNs using position-only, vision-only, and multimodal inputs. Our results show that compact ANNs consistently outperform SNNs for position-only sensing. In contrast, for vision and multimodal sensing, SNNs achieve higher accuracy than ANNs at similar energy costs and require up to an order of magnitude less energy at a comparable accuracy. For example, the multimodal SNN achieves 58.08% top-1 accuracy, outperforming the closest-energy ANN by 18.11 percentage points. We further show that SNN accuracy is limited by the binary communication between layers rather than by model size. These results demonstrate the potential of SNNs for energy-efficient sensing-aided beam prediction while highlighting their accuracy limitation.
| Comments: | Submitted for possible publication |
| Subjects: | Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.12507 [cs.NE] |
| (or arXiv:2610.12507v1 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12507 arXiv-issued DOI via DataCite |
Submission history
From: Nhan Thanh Nguyen [view email]
[v1]
Tue, 6 Oct 2026 08:13:54 UTC (81 KB)
来源：arXiv cs.AI · arxiv.org