The paper introduces GCNO, a physics‑based, variable‑rate neural operator that compresses wireless channel matrices by identifying a sample‑dependent set of dominant propagation paths instead of treating the matrix as an image. GCNO leverages receive‑transmit channel structure, a first‑order Taylor correction, and least‑squares recovery to encode path directions and strengths, and the base station reconstructs the channel analytically from these tuples. Experiments on three ray‑traced environments show GCNO outperforms neural feedback baselines in reconstruction accuracy for the same payload or achieves the same accuracy with lower payload, and it generalizes to unseen antenna counts without retraining.
By Rafid Umayer Murshed, Shahab Hamidi-Rad, Elahe Soltanaghai, Akshay Malhotra
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
The paper introduces Physics-Unrolled Hybrid Neural Operator (PU‑HNO), a three‑stage cascade that transforms low‑fidelity ray‑tracing outputs and scene priors into high‑fidelity indoor radio maps by sequentially modeling reflection, diffraction, and scattering. It demonstrates that, under conditionally unbiased label noise, the model can learn stable propagation structures and surpass its own training labels. Experiments on varied floorplans show PU‑HNO outperforming image‑to‑image baselines, wireless learning models, and monolithic neural operators in both image quality and wireless deployment metrics.
By Rafid Umayer Murshed, Saif Ur Rahman, Mingyue Tang, Elahe Soltanaghai
arXiv:2506. 18295v2 Announce Type: replace-cross Abstract: Neural ray tracing (RT) has emerged as a promising paradigm for channel modeling by integrating physical propagation principles with neural networks.
By Kejia Bian, Meixia Tao, Shu Sun, Tongjia Zhang, Jun Yu
arXiv:2610.19524v1 Announce Type: new
Abstract: Accurate path loss (PL) estimation is essential for wireless network planning and optimization. This paper proposes HankelKANet, a lightweight physics-...
By Yue Ruan, Xiaoxiao Cheng, Yunan Zhu, Wenlong Huang, Meng Hua
The paper introduces a self‑localizing MIMO beam‑mapping framework that builds a hierarchical wireless memory using sparse channel state information (CSI) without explicit location labels. It employs beam‑domain RSS as compact inputs, a dual‑scale extractor for angular and temporal dependencies, and a hybrid temporal encoder to infer physical anchors that index a structured radio map. The radio‑map embedding enables continuous updates and full‑CSI reconstruction, yielding over 30% better anchor recovery and more than 20% channel‑capacity gains in NLOS beam tracking compared to Kalman‑filter methods.
By Wangqian Chen, Junting Chen, Shuguang Cui