arXiv:2406. 04737v2 Announce Type: cross Abstract: The cellular network of magnetic Induction (MI) communication holds promise in long-distance underground environments.
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arXiv:2510. 14854v4 Announce Type: cross Abstract: Magnetic induction (MI) communication (MIC) has emerged as a promising candidate for underground communication networks due to its excellent penetration capabilities.
The paper investigates federated learning over multiple‑access fading channels with a multi‑antenna base station, focusing on user‑level differential privacy (DP). It shows that DP can be achieved as a natural benefit of channel noise without adding artificial noise, deriving new DP and convergence bounds for general bounded‑domain models and non‑convex loss functions. The authors further optimize beamforming and power allocation to characterize the trade‑off between privacy and convergence, providing theoretical insights and numerical validation.
The paper proposes an XL‑MIMO system with stacked intelligent metasurfaces (SIM) that functions as a two‑layer neural network operating entirely over the air. The first layer is the uncontrollable wireless fading, while the second controllable layer is implemented via SIM operations, requiring only the front‑end metasurface to provide a nonlinear activation. Using the Extreme Learning Machine framework, the authors derive a closed‑form optimal linear SIM combining, enabling fast training and demonstrating classification accuracy comparable to digital models across various datasets.
arXiv:2610.19524v1 Announce Type: new Abstract: Accurate path loss (PL) estimation is essential for wireless network planning and optimization. This paper proposes HankelKANet, a lightweight physics-...
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 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.