arXiv Machine Learning
Geometry-Aided Channel Deduction with Partial Channel Estimates and Uncalibrated Digital Twin
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arXiv Machine Learning
Jul 24
Environment-Aware Channel Inference via Cross-Modal Flow: From Multimodal Sensing to Wireless Channels
arXiv:2512. 04966v2 Announce Type: replace-cross Abstract: Accurate channel state information (CSI) underpins reliable and efficient wireless communication.
arXiv AI
Aug 25
DRIFT: Joint Channel Estimation and Prediction Towards Pilotless 6G Non-Terrestrial Networks
The paper introduces DRIFT, a lightweight framework for joint channel estimation and prediction in low Earth orbit non-terrestrial networks, aiming to reduce pilot overhead by using data-driven processing after the initial slot. DRIFT refines data-aided channel estimates and forecasts future channel responses with low computational cost, offering two variants based on convolutional and LSTM layers. Simulations show up to 12% spectral efficiency gain over conventional pilot-based systems, with under 200k multiply-accumulate operations suitable for on-board satellite implementation.
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.
arXiv Machine Learning
Aug 12
Null-Space Flow Matching for MIMO Channel Estimation in Latency-Constrained Systems
arXiv:2604. 22005v2 Announce Type: replace-cross Abstract: Accurate yet low-latency channel state information (CSI) acquisition is essential for multiple-input multiple-output (MIMO) communication systems.
arXiv AI
Jul 17
CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models
arXiv:2607. 14975v1 Announce Type: new Abstract: Channel foundation models (CFMs) are developing rapidly, with recent studies reporting benefits from pretraining across downstream wireless tasks.
arXiv AI
Sep 15
Adaptive 3D-RoPE: Physics-Aligned Rotary Positional Encoding for Wireless Foundation Models
arXiv:2605.00968v2 Announce Type: replace-cross Abstract: Wireless foundation models (WFMs) have emerged as a promising paradigm for unified channel state information (CSI) acquisition across diverse...