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arXiv AI

AntennaFlow: A Generative Flow Model for Offset Correction in Phaseless Antenna Testing

AntennaFlow is a three‑stage generative flow model that jointly addresses costly phase acquisition and offset‑mounting violations in near‑field to far‑field antenna testing. It learns an offset‑invariant embedding of amplitude fields, transports offset amplitudes to a center‑aligned field, and applies a simplified extrapolation technique valid only for centered fields. Experiments demonstrate fast, phaseless, offset‑vector‑free NF–FF reconstruction from sparse amplitude‑only measurements, outperforming existing baselines while preserving physical consistency.

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arXiv Machine Learning
Sep 25

Physics-Guided Multi-Objective Deep Learning for Ultrasound RF Data Interpolation in Resource-Constrained Imaging

The paper introduces a physics‑guided, data‑driven framework for reconstructing dense ultrasound RF data from sparse acquisitions. It trains an end‑to‑end interpolation network with a hybrid RF‑ and beamforming‑domain loss, stabilized by exponential moving average, and employs random‑skip masking to generalize across varying sparsity patterns and channel configurations. On a held‑out test set, the method achieves a mean SSIM of about 0.95 across decimation factors from ×2 to ×13, consistently improving RF reconstruction and post‑beamforming image quality.

By Luoyuan Zhang, Yiyang You, Ananya Tandri, Yinan Feng, Hyunwoo Song, Jeeun Kang, Youzuo Lin
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arXiv Machine Learning
Jul 24

RadioTrace: Transmitter-Aware Diffusion for Radio Map Estimation without Deployment-Time Fine-Tuning

arXiv:2607. 20909v1 Announce Type: cross Abstract: Radio map (RM) estimation aims to reconstruct the spatial distribution of wireless signal characteristics, such as received signal strength (RSS), from sparse measurements, a task that is critical for spectrum management, interference mitigation, and localization in modern wireless networks.

By Liu Yang, Qiang Li, Zhuo Cao, Weijie Xiong, Guomin Sun, Jingran Lin
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arXiv Machine Learning
Sep 22

WiNeRF: Measurement Constrained Radiance Fields for Actionable Wireless Channel Modeling

WiNeRF is a neural field framework that learns a spatially continuous, complex-valued wireless channel representation from sparse channel state information collected by commodity WiFi devices. It incorporates system constraints such as antenna geometry, limited spatial resolution, and phase uncertainty through a 3D conical wave sampling model, a multi-resolution implicit scene representation, and a differentiable optimization framework. In diverse indoor environments with non‑line‑of‑sight regions, WiNeRF achieves a median prediction SNR of 5.3 dB, outperforming prior neural baselines by 4.9 dB on average, and produces a task‑agnostic channel representation that can be reused in standard signal‑processing pipelines without hardware or protocol changes.

By Saif Ur Rahman, Rafid Umayer Murshed, Anton Dmitriev, Cagri Tanriover, Rahul C. Shah, Elah\'e Soltanaghai
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arXiv AI
Jun 29

Exposure Bias Can Alleviate Itself via Directional and Frequency Rectification in Flow Matching

arXiv:2606. 28226v1 Announce Type: cross Abstract: Flow Matching (FM) has achieved remarkable generative performance, yet it suffers from exposure bias due to discrepancies between training and inference.

By Guanbo Huang, Jingjia Mao, Fanding Huang, Fengkai Liu, Xiangyang Luo, Yaoyuan Liang, Jiasheng Lu, Xiaoe Wang, Pei Liu, Ruiliu Fu, Ruqi Huang, Shao-Lun Huang
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