GCNO: Gramian Chebyshev Neural Operator for Physics-Based Compression of Wireless Channels proposes a variable‑rate compressor that reports only the dominant propagation paths of a wireless channel instead of a full complex‑valued matrix. The method uses the channel’s receive‑transmit structure to locate paths, applies a first‑order Taylor correction for sub‑grid directions, and recovers path strengths via least squares, all trained without explicit path labels. 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 a lower payload, and it generalizes to unseen antenna counts without retraining.
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: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
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:2507. 09627v3 Announce Type: replace-cross Abstract: Next-generation wireless technologies such as 6G aim to meet demanding requirements such as ultra-high data rates, low latency, and enhanced connectivity.
By Muhammad Kamran Saeed, Ashfaq Khokhar, Shakil Ahmed