arXiv AI
Jun 6
LatentWave: JEPA Pretraining for Wireless Foundation Models
arXiv:2606. 06373v1 Announce Type: cross Abstract: Wireless foundation models have emerged as a promising alternative to building separate models for each wireless task.
JEPA-Inspired Latent Field Prediction for Sparse-Pilot MIMO-OFDM Channel Reconstruction
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.
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arXiv Machine Learning
Sep 22
WiNeRF: Measurement Constrained Radiance Fields for Actionable Wireless Channel Modeling
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.
arXiv Machine Learning
Aug 4
Hybrid-Field Sparse Channel Representation and Recovery for XL-RIS-Assisted mmWave MIMO Systems
arXiv:2608. 00052v1 Announce Type: cross Abstract: Extremely large-scale reconfigurable intelligent surface (XL-RIS)-assisted communication is regarded as a key enabling technology for future 6G networks.
arXiv AI
Aug 20
Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition
The paper introduces a hybrid estimator for pilot‑limited MIMO channel estimation that frames the problem as low‑rank tensor completion from sparse pilot data. It compares Canonical Polyadic (CP) and Tucker decompositions, showing CP excels for specular channels while Tucker offers stability under extreme pilot scarcity. A lightweight 3D U‑Net is added to capture residual components, and the combined Tensor–NN approach achieves significant NMSE improvements over conventional methods across various pilot densities and channel models.
arXiv Machine Learning
Sep 30
Geometry-Aided Channel Deduction with Partial Channel Estimates and Uncalibrated Digital Twin
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...
arXiv Machine Learning
Aug 12
Null-Space Flow Matching for MIMO Channel Estimation in Latency-Constrained Systems
arXiv:2604. 22005v2 Announce Type: replace-cross Abstract: Accurate yet low-latency channel state information (CSI) acquisition is essential for multiple-input multiple-output (MIMO) communication systems.
arXiv AI
Jul 8
Contrastive Predictive Coding with Compression for Enhanced Channel State Feedback in Wireless Networks
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.
arXiv AI
Aug 25
DRIFT: Joint Channel Estimation and Prediction Towards Pilotless 6G Non-Terrestrial Networks
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.
arXiv AI
Sep 17
Semantic CSI Feedback for Beam Selection: When Task-Aware Embeddings from Sparse Pilots Outperform Full-Bandwidth Reconstruction
The paper introduces a semantic communication approach for CSI feedback in FDD massive MIMO, where the UE sends a learned embedding optimized for beam selection rather than reconstructing the full channel. Experiments show that an 8‑dimensional embedding derived from just 43 NR CSI‑RS pilots in the angular‑delay domain achieves the best beam prediction accuracy, surpassing methods that use the entire 512‑subcarrier channel. This demonstrates that beam‑relevant information is inherently low‑dimensional, allowing the semantic encoder to discard irrelevant details and transmit only the intent needed for beam selection.
arXiv Machine Learning
Oct 12
JEPA-kMMVAE: CSI Prediction for Time-Varying Wireless Channels
The paper introduces JEPA-kMMVAE, a neural network framework for predicting channel state information (CSI) in time‑varying multi‑antenna wireless channels using noisy historical observations. It combines a joint‑embedding predictive architecture with a k‑memory Markov variational autoencoder to generate temporally structured latent representations, enabling both direct prediction and residual corrections relative to the latest latent state and noisy CSI. Simulation results indicate that the residual configuration outperforms baseline methods at moderate and high input CSI signal‑to‑noise ratios, while sequence baselines remain more accurate under stronger noise.
arXiv AI
Sep 15
Adaptive 3D-RoPE: Physics-Aligned Rotary Positional Encoding for Wireless Foundation Models
arXiv:2605.00968v2 Announce Type: replace-cross Abstract: Wireless foundation models (WFMs) have emerged as a promising paradigm for unified channel state information (CSI) acquisition across diverse...
arXiv AI
Jun 3
Non-Identical Diffusion Models in MIMO-OFDM Channel Generation
arXiv:2509. 01641v3 Announce Type: replace-cross Abstract: We propose a novel diffusion model, termed the non-identical diffusion model, and investigate its application to wireless orthogonal frequency division multiplexing (OFDM) channel generation.