The paper proposes a method to reduce the number of ports that need to be probed in fluid antenna systems by selecting a representative subset of ports offline. By collecting optimal ports from a set of training channel realizations, the authors show that only one scan of the candidate ports is required per new realization. In a 7×7 DeepMIMO fluid antenna system with 49 candidate ports, 120 training realizations produce an average of 27 representative ports while preserving 99.995% of the channel power gain that would be achieved by exhaustive port selection.
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.
arXiv:2607. 06768v1 Announce Type: cross Abstract: This paper investigates over-the-air federated learning (AirFL) in wireless systems where the access point is equipped with a multi-waveguide pinching antenna system (PASS).
The paper introduces CoNN-AL, a framework that combines stream-based active learning with a Cooperative Neural Network and Denoising Autoencoder to efficiently perform partial inverse design in engineering. By estimating predictive uncertainty with Monte Carlo dropout, the method selectively labels the most informative samples in real time, dramatically reducing the number of required simulations. Applied to an automotive glass run channel dataset of over 900,000 designs, CoNN-AL achieves R-squared values between 0.967 and 0.982 using only 2.3% of the full training set, outperforming random sampling and approaching the performance of models trained on much larger datasets.
arXiv:2609.08312v1 Announce Type: cross Abstract: To mitigate the scalability bottleneck in the radio access network (RAN) in federated edge learning (FEEL), over-the-air federated learning (AirFL) e...
The paper introduces GCNO, a physics‑based, variable‑rate neural operator that compresses wireless channel matrices by identifying a sample‑dependent set of dominant propagation paths instead of treating the matrix as an image. GCNO leverages receive‑transmit channel structure, a first‑order Taylor correction, and least‑squares recovery to encode path directions and strengths, and the base station reconstructs the channel analytically from these tuples. Experiments on three ray‑traced environments show GCNO outperforms neural feedback baselines in reconstruction accuracy for the same payload or achieves the same accuracy with lower payload, and it generalizes to unseen antenna counts without retraining.
arXiv:2511. 20305v2 Announce Type: replace-cross Abstract: This paper investigates a reconfigurable intelligent surface (RIS)-assisted multi-waveguide pinching-antenna (PA) system (PASS) for multi-user downlink information transmission, motivated by the unknown impact of the integration of emerging PASS and RIS on wireless communications.
arXiv:2609. 38023v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) embed governing equations into deep learning, but enforce them only through loss residuals, leaving highly oscillatory wave behavior to be discovered by optimization.
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.
The paper tackles the challenge of converting uplink to downlink channel covariance matrices in FDD massive MIMO systems, where learning‑based methods lose accuracy as the antenna count grows. It identifies that the mean angle of arrival creates a phase ramp whose oscillation rate increases with array size, making fixed‑size datasets sparse. The authors propose a deramping technique that estimates and removes this ramp before learning, reducing estimation error across three different learners and maintaining superiority over model‑based benchmarks at large array sizes.
arXiv:2610.10235v2 Announce Type: replace-cross Abstract: Fluid antenna systems (FASs) offer significant spatial flexibility, yet securing them against eavesdropping is critical for practical FAS dep...
The paper introduces Tri-PNet, a deep learning framework that jointly designs electromagnetic (EM), analog, and digital precoding for tri-hybrid multi-user MIMO OFDM systems. By leveraging Conformer architecture, Tri-PNet learns to select radiation patterns and perform hybrid precoding, achieving higher spectral efficiency than random EM precoding and conventional hybrid MIMO while maintaining low online complexity. The study evaluates two radiation-pattern modes—non-regular and 3GPP TR 38.901—showing robust performance even with imperfect channel state information.
The paper introduces a method to enrich Radio Foundation Models (RFMs) by incorporating channel configuration (CFG) information during pre‑training. These CFG‑enriched embeddings are shown to act as effective inductive priors for downstream tasks, especially when labeled data are scarce. Experiments across three data regimes and multiple tasks—LoS/NLoS classification, timing advance estimation, and beam selection—demonstrate that the new embeddings consistently outperform or match baseline models in both in‑distribution and out‑of‑distribution configuration settings.