The paper proposes an eXtremely Large MIMO system that functions as an Extreme Learning Machine for over‑the‑air binary classification. It uses cascaded metasurfaces, with a front layer providing a fixed nonlinear activation and subsequent tunable linear layers implementing trained weights directly in the wave domain. Numerical results on various datasets show that this low‑complexity, wave‑domain architecture achieves classification accuracy comparable to ideal digital models.
By Kyriakos Stylianopoulos, Mattia Fabiani, Giulia Torcolacci, Davide Dardari, George C. Alexandropoulos
arXiv:2602. 11834v2 Announce Type: replace-cross Abstract: While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial multiplexing order and lack of explainability and generalization.
By Mikko Honkala, Dani Korpi, Elias Raninen, Janne M. J. Huttunen
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.
By Muhammad Kamran Saeed, Ashfaq Khokhar, Shakil Ahmed
The paper proposes using analog radio frequency (RF) computing to enable edge devices to perform AI inference by receiving a waveform that encodes neural network weights from a base station. Clients then mix this waveform with their own input-encoded signals to perform matrix‑vector multiplications with minimal power and memory. In a multi‑user MIMO downlink setting, the authors formulate a joint beamforming optimization to balance data and AI flows, showing that the aggregate data rate loss is below 5% under realistic 3GPP channel conditions.
By Wentao Yu, Vincent W. S. Wong
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.
By Kaijun Feng, Jiaxin He, Hongrui Yu, Zhen Gao, Anwen Liao, Ziwei Wan, Zhaocheng Wang
arXiv:2607. 16877v1 Announce Type: cross Abstract: The increasing complexity of next-generation wireless networks has driven the integration of artificial intelligence (AI) into wireless communications.
By Yangjing Wang, Ouya Wang, Shenglong Zhou, Geoffrey Ye Li