Energy-Efficient Spiking Neural Networks for Sensing-Aided Beam Prediction
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...
| Authors | Nhan Thanh Nguyen, Phuong Nam Tran, Mengyuan Ma et al. |
|---|---|
| Primary category | cs.NE |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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