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.
By Bruno De Filippo, Carla Amatetti, Alessandro Vanelli-Coralli
arXiv:2607. 28994v1 Announce Type: cross Abstract: High-fidelity radio fields are typically simulated for every scene--transmitter configuration or fitted separately to each scene, failing to exploit propagation structures shared across environments.
By Chaozheng Wen, Chenghong Bian, Hongze Chen, Jun Zhang
arXiv:2608. 09285v1 Announce Type: cross Abstract: Learning-based wireless localizers often fail to utilize geometric information about the propagation environment, limiting their ability to exploit non-line-of-sight (NLoS) propagation and generalize across scenes.
By Chenghong Bian, Chaozheng Wen, Hongze Chen, Jun Zhang
arXiv:2512. 04966v2 Announce Type: replace-cross Abstract: Accurate channel state information (CSI) underpins reliable and efficient wireless communication.
By Guangming Liang, Mingjie Yang, Dongzhu Liu, Paul Henderson, Lajos Hanzo
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
arXiv:2511. 05522v4 Announce Type: replace-cross Abstract: Accurate, low-latency channel modeling is essential for real-time wireless network simulation and digital-twin applications.
By Ali Saeizadeh, Miead Tehrani-Moayyed, Davide Villa, J. Gordon Beattie Jr., Pedram Johari, Stefano Basagni, Tommaso Melodia
The paper presents a channel-informed neural network for physical-layer key generation (PKG) that extracts binary key features directly from IQ measurements while grounding the representation in the multipath channel. The multi-task recurrent network jointly learns reciprocity-preserving features and an auxiliary channel estimate, using deep metric learning and channel-informed supervision. Experiments on indoor and outdoor software-defined-radio data show lower bit disagreement for legitimate users, improved key diversity with ray-traced augmentation, and successful NIST randomness tests after SHA-3 privacy amplification.
By Jose Angel Sanchez Viloria, George Sklivanitis, Dimitris Pados, Elizabeth Serena Bentley
arXiv:2608. 05896v1 Announce Type: new Abstract: Beamforming plays a key role in multiple-input-multiple-output (MIMO) communication systems.
By Yijie Bian, Wei Guo, Zixin Wang, Shenghui Song, Jun Zhang, Khaled B. Letaief
arXiv:2609.37277v1 Announce Type: cross
Abstract: The acquisition of high-dimensional channel state information (CSI) in wireless MIMO-OFDM communications usually requires high pilot overhead, or rel...
By Hongning Ruan, Zhaoyang Zhang, Zirui Chen, Ziqing Xing, Zhaohui Yang, M\'erouane Debbah
arXiv:2507. 19349v3 Announce Type: replace Abstract: As sixth generation (6G) wireless networks evolve, accurate signal-to-interference-noise ratio (SINR) maps are becoming increasingly critical for effective resource management and optimization.
By Lorenzo Mario Amorosa, Francesco Conti, Nicola Quercioli, Flavio Zabini, Tayebeh Lotfi Mahyari, Yiqun Ge, Patrizio Frosini
arXiv:2607. 14127v1 Announce Type: cross Abstract: Representative clutter height (RCH) is a key parameter in radio propagation and interference analysis because it captures the dominant height of local obstructions that drive terminal clutter loss.
By Shohini Sarkar, Smithi Mahendran, Rishi Chudasama, Varun Mannam, Arav Luthra, Yuvraj Rekhi, Vivek Nadig, Arsh Goenka
arXiv:2608. 04050v1 Announce Type: cross Abstract: Wi-Fi networks are moving beyond random channel access toward tightly coordinated operation across access points (APs), a shift reflected in Wi-Fi 8's multi-AP coordination (MAPC).
By Maksymilian Wojnar, Krzysztof Rusek, Katarzyna Kosek-Szott, Szymon Szott