arXiv AI October 12, 2026 By Nhan Thanh Nguyen, Phuong Nam Tran, Mengyuan Ma, Huy T. Nguyen, Ahmed Alkhateeb, Markku Juntti

Energy-Efficient Spiking Neural Networks for Sensing-Aided Beam Prediction

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The paper presents energy‑efficient spiking neural network (SNN) architectures for sensing‑aided beam prediction in millimeter‑wave systems, comparing them with conventional artificial neural networks (ANNs) across position‑only, vision‑only, and multimodal inputs. While compact ANNs outperform SNNs for position‑only data, SNNs achieve higher accuracy than ANNs for vision and multimodal sensing at comparable or lower energy costs, with the multimodal SNN reaching 58.08% top‑1 accuracy—an 18.11‑percentage‑point improvement over the nearest‑energy ANN. The study also finds that SNN accuracy is constrained by binary inter‑layer communication rather than model size.

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