arXiv:2606. 16607v1 Announce Type: cross Abstract: This paper considers neural channel state information (CSI) compression for time-varying massive multiple-input multiple-output (MIMO) channels in frequency division duplex (FDD) systems with limited feedback resources.
By Efstathios Chatziloizos, Konstantinos Vandikas, Aneta Vulgarakis Feljan, Zheng Chen, Nikolaos Pappas
arXiv:2606. 06373v1 Announce Type: cross Abstract: Wireless foundation models have emerged as a promising alternative to building separate models for each wireless task.
By Ahmed Mohamed, Ahmed Aboulfotouh, Hatem Abou-Zeid
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 introduces a diffusion-based channel estimation framework for MIMO systems that incorporates temporal dynamics via an LSTM-conditioned denoiser. It employs a learnable SNR-gated fusion mechanism to balance observation fidelity with generative priors across a wide SNR range, and uses DDIM-style reverse updates with SNR-adaptive truncation to reduce inference latency. Simulations on standardized time-evolving channel models show consistent performance gains over existing diffusion-based baselines while maintaining low latency.
By Jixing Zhou, Xinming Huang
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
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