Comparative Evaluation of Deep-Learning based DoA Estimators for Real-Time ISAC Beamforming
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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arXiv:2607. 11970v1 Announce Type: cross Abstract: We develop an enhanced in-context learning (ICL) framework to improve the performance of pilot-based beamforming in multi-user multiple-input single-output (MU-MISO) systems.
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.
arXiv:2607. 00860v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging.
arXiv:2611.01491v2 Announce Type: replace-cross Abstract: Future 6G wireless networks are expected to evolve toward AI-native systems that support intelligent, environment-aware services, including p...
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.
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.