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:2608. 05076v1 Announce Type: cross Abstract: Recent advances in machine learning have enabled training of wireless foundation models, which aim to support tasks such as channel estimation, beam prediction, and localization based on wireless signals.
By Blessed Guda, Kayley Sze, Carlee Joe-Wong
arXiv:2607. 05419v1 Announce Type: cross Abstract: Accurate and timely channel state information (CSI) is essential for next-generation wireless systems, yet existing works treat CSI compression and CSI prediction as separate problems, both in academia and in current 3GPP studies.
By Ahmed Y. Radwan, Hina Tabassum, Fahad Syed Muhammad, Matthew Baker
The paper proposes a dynamic channel sounding framework for Wi‑Fi MIMO systems that combines autoregressive CSI prediction with a Transformer-based model to forecast channel state information across multiple subcarriers. It introduces a SINR‑constrained interval selection strategy that maximizes predicted average throughput while accounting for acquisition and processing overhead, and validates the approach using Sionna‑based ray tracing and Wi‑Fi 7 link‑level simulations. The results show significant goodput gains—up to 119.1%—over stale CSI for various mobility trajectories.
By Lyutianyang Zhang, Yixin Ying, Liu Cao, Vanlin Sathya, Jinke Ren, Shuguang Cui
Recent advances in machine learning have enabled training of wireless foundation models, which aim to support tasks such as channel estimation, beam prediction, and localization based on wireless signals. Existing wireless foundation models typically pretrain on channel tensors using masked reconstruction over subcarriers, antennas, or time but ignore the physical characteristics of wireless propagation.
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.
By Yuan Gao, Wenjun Yu, Jun Jiang, Yunfan Li, Xinyu Guo, Shugong Xu
The paper introduces a JEPA-inspired latent field prediction model for reconstructing MIMO‑OFDM channels from sparse pilots. It maps observed CSI into latent time‑frequency tokens, predicts masked or future latent states using historical multi‑band context, and fuses these predictions with current noisy pilots to recover the full channel. Ray‑tracing experiments demonstrate that this approach reduces normalized mean square error compared to pilot‑only and direct reconstruction baselines.
By Yuzhi Yang, Brahim Mefgouda, Hang Zou, Lina Bariah, Anis Bara, Yuhuan Lu, Hao Zhang, Merouane Debbah
The paper introduces MiLAAP, an attention‑based framework that predicts channel occupancy and node motion in channel‑hopping communication systems without exchanging state information. By leveraging self‑attention and multi‑head attention, each node passively observes local channel activity to forecast interference patterns and mobility, achieving near‑perfect prediction accuracy across varied mobility scenarios and demonstrating zero‑shot generalizability to new channel‑sequence periods.
By Yung-Fu Chen, Anish Arora
The paper introduces NOSTRAdAMUS, a predictive link‑adaptation framework for 5G NR that forecasts retransmissions in the next radio frame using recent HARQ history and adjusts the Modulation and Coding Scheme accordingly. Gradient Boosting models achieve 82.9% overall accuracy, with high‑confidence predictions correct 94.2% of the time and a 5.5 µs inference latency. Evaluated OTA on the X5G testbed and various channel emulators, the approach boosts goodput by up to 71.5% and cuts retransmissions by up to 71.8% without retraining across diverse scenarios.
By Tamerlan Aghayev, Maxime Elkael, Michele Polese, Reshma Prasad, Salvatore D'Oro, Yunseong Lee, Koichiro Furueda, Tommaso Melodia
arXiv:2607. 08454v1 Announce Type: cross Abstract: Coordinated beamforming in distributed 5G networks relies on the timely exchange of inter-cell scheduling information, but backhaul latency makes this information stale.
By Prashant Kumar Singh, Shubham Vaishnav, Ahmet Hasim G\"okceoglu, Li Wang
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 TRACE-CRC, a trajectory‑adaptive conformal risk control method for multi‑step channel state information (CSI) prediction. It builds Frobenius‑norm uncertainty balls around predicted CSI matrices and controls the risk that any future frame is uncovered, using future‑step‑dependent error profiling, trajectory difficulty stratification, and learn‑then‑test risk control. Experiments show that TRACE‑CRC delivers reliable trajectory‑level coverage with smaller uncertainty balls than conservative multi‑step corrections and avoids undercoverage seen in stepwise and adaptive baselines.
By Kiarash Rezaei, Mehdi Sattari, Javad Aliakbari, Tommy Svensson, Paolo Monti, Carlos Natalino