arXiv Machine Learning
Aug 5

Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networks

arXiv:2608. 03319v1 Announce Type: cross Abstract: Future integrated sensing and communication (ISAC) architectures separate the sensing entity (SE) that acquires measurements from the sensing function (SF) that performs inference, creating a need for compact, task-oriented feedback on the SE-SF interface.

By Shiv Shankar, Radha Krishna Ganti, J Klutto Milleth
arXiv AI
Oct 12

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

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.

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

CNN-Transformer with Sigmoid Label for High-Resolution Sub-Degree DOA Estimation

The paper introduces a sigmoid label for deep‑learning based direction‑of‑arrival (DOA) estimation, encoding DOAs as sums of angle‑shifted monotonic sigmoid functions. Using a CNN‑Transformer network trained with this label, the authors achieve sub‑degree accuracy and 1‑degree resolution with a 0.25° grid step, surpassing traditional Gaussian, hard, and BCE labels. Numerical results confirm the sigmoid label’s superior accuracy and resolution over other labeling approaches.

By Lifeng Wang, Xintong Li, Aifei Liu