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
arXiv:2602. 11834v2 Announce Type: replace-cross Abstract: While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial multiplexing order and lack of explainability and generalization.
By Mikko Honkala, Dani Korpi, Elias Raninen, Janne M. J. Huttunen
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 presents a large‑scale, 3GPP TR 38.901‑compliant dataset for RIS‑aided millimeter‑wave B5G networks, covering 20 deployment variants with diverse user densities, fading, and blockage conditions. Each sample includes oracle RIS phase configurations from a brute‑force search, along with full CSI, per‑link channel decomposition, optimal phase matrices, and CQI labels, enabling a wide range of machine‑learning tasks. The authors also introduce a novel CSI‑to‑CQI mapping as a benchmark for scalable link‑quality prediction and evaluate it against state‑of‑the‑art models under various conditions.
By Pujitha Mamillapalli, Pankaj Singh Rathour, Abhinav Kumar
arXiv:2606. 28778v1 Announce Type: cross Abstract: In next-generation wireless networks, the growing density of devices and limited spectrum resources pose severe jamming challenges to fragile legitimate communication links in the wireless electromagnetic environment.
By Honghan She, Yufan Cheng, Tieming Sun, Pengyu Wang, Siya Huang, Kaikai Yang
arXiv:2607. 01660v1 Announce Type: new Abstract: Hardware impairments in massive multiple-input multiple-output (MIMO) receivers introduce inter-symbol memory and inter-element coupling, severely degrading channel estimation.
By Wei Xu, An Liu