LATIDIA · Investigación

Redes neuronales de picos de alta eficiencia energética para la predicción de haces asistidos por detección

arXiv: 2610.12507v1Tipo de anuncio: Cross Resumen: La predicción del haz asistida por detección puede reducir la sobrecarga de entrenamiento del haz de los sistemas de onda milimétrica (mmWave) al aprovechar la información ambiental de la modalidad complementaria

La noticia

arXiv:2610.12507v1 Announce Type: cross 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