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.
arXiv:2607. 04471v1 Announce Type: cross Abstract: Linear spatial filters (beamformers) enable robust, generalizable and interpretable speech enhancement with performance guarantees under ideal parameterization.
arXiv:2607. 19609v1 Announce Type: cross Abstract: In this paper, we study an unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) system, where a UAV enhances the sensing capability of a base station (BS) towards a target while ensuring reliable communication towards a downlink user.
arXiv:2607. 09798v1 Announce Type: cross Abstract: Sixth-generation (6G) networks are moving toward AI-native operation, where learning modules are embedded across the radio access network (RAN), edge, and core.
In marine seismic acquisition, seismic interference (SI) occurs when energy from nearby external seismic source(s) is captured. It typically appears as coherent noise with linear or non-linear movemen...
The paper proposes using analog radio frequency (RF) computing to enable edge devices to perform AI inference by receiving a waveform that encodes neural network weights from a base station. Clients then mix this waveform with their own input-encoded signals to perform matrix‑vector multiplications with minimal power and memory. In a multi‑user MIMO downlink setting, the authors formulate a joint beamforming optimization to balance data and AI flows, showing that the aggregate data rate loss is below 5% under realistic 3GPP channel conditions.
The paper proposes a method for learning nonlinear waveform fusion across scales to improve integrated sensing and communication (ISAC). By adaptively weighting temporal features and updating spectral components, the authors generate a waveform that maintains fixed total energy while combining image features with a deterministic frequency‑modulated reference. Experiments on 512 test images show that the learned waveforms reduce normalized localization error by 29.0 % and 27.0 % and increase mean peak SNR by 1.40 dB and 0.82 dB compared to baseline approaches.