The paper introduces SR‑TTA, a spatial‑redundancy test‑time adaptation method for respiration sensing using a 64‑antenna cell‑free massive MIMO base station. It replaces hand‑crafted fusion with a learned complex‑weight beamformer that spatially nulls in‑band motion interference, and further closes the performance gap to an oracle through label‑free adaptation. Experiments show the respiration‑rate error drops from 5.8 to 0.8 breaths per minute under simulated interference, and real‑world recordings see failure rates fall from 47% to 7% with a single cross‑subject calibration.
By Jingyuan Liu, Zheng Chang, Haoqiu Xiong, Zhuangzhuang Cui, Sofie Pollin
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:2607. 08717v1 Announce Type: new Abstract: Narrowband interference (NBI) severely degrades orthogonal frequency-division multiplexing (OFDM) systems by corrupting subcarriers and rendering classical soft demodulation ineffective.
By Emmanouil Kavvousanos, Francky Catthoor, Vassilis Paliouras
arXiv:2607. 27450v1 Announce Type: new Abstract: Accurate multipath parameter estimation is critical for modern wireless communication systems, particularly in challenging low-SNR environments.
By Chaofan Deng, Linyu Sun, Jaeho Lee, Arijit Raychowdhury
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.
By Kyriakos Stylianopoulos, Mattia Fabiani, Davide Dardari, George C. Alexandropoulos
The paper introduces EXT-ICLNet, a Transformer-based detector for MIMO turbo receivers that uses pilots as in-context examples to learn the symbol extrinsic posterior distribution without explicit channel estimation. By implementing a log-domain prior skip connection, the network integrates seamlessly with existing turbo receivers, learning only extrinsic information. Numerical experiments on a 4×4 MIMO system with 16‑QAM and a quantized front end show that EXT-ICLNet outperforms baselines relying on explicit CSI estimation at low‑to‑medium SNRs and EP baselines with perfect CSI at high SNRs, as confirmed by EXIT chart comparisons.
By Zihang Song, Matteo Zecchin, Bipin Rajendran, Petar Popovski, Osvaldo Simeone
arXiv:2606. 28778v1 Announce Type: cross Abstract: In next-generation wireless networks, the growing density of devices and limited spectrum resources pose severe jamming challenges to fragile legitimate communication links in the wireless electromagnetic environment.
By Honghan She, Yufan Cheng, Tieming Sun, Pengyu Wang, Siya Huang, Kaikai Yang
arXiv:2609.07762v1 Announce Type: cross
Abstract: Recurrent detectors such as bidirectional long short-term memory (Bi-LSTM) networks are low-complexity alternatives to the optimal Bahl-Cocke-Jelinek...
By Nurettin Safak, Osman Tokluoglu, Enver Cavus
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
CRFCAN is a complex‑valued residual FFT convolutional attention network that jointly estimates channel and phase noise in sub‑THz OFDM systems. It embeds FFT and inverse FFT modules within residual groups to enable iterative feature interaction across time and frequency domains, and includes dedicated residual blocks for complex feature extraction and multiplicative phase‑distortion modeling. Simulation results show that CRFCAN outperforms conventional algorithms and state‑of‑the‑art deep learning models in NMSE and BER, while offering single‑shot, fixed‑complexity inference and good generalization to unseen phase‑noise models.
By Ruilin Wang, Xiaodai Dong
arXiv:2610.19490v1 Announce Type: cross
Abstract: Digital-domain self-interference cancellation is essential in in-band full-duplex radios after passive suppression and analog cancellation. It is par...
By Zhijie Su, Shiquan Zhang, Qiaolin He
arXiv:2511. 17007v2 Announce Type: replace-cross Abstract: Open and intelligent radio access networks (RANs) envisioned for 6G require accurate and reusable wireless channel knowledge for intelligent inference and control.
By Wangqian Chen, Junting Chen, Shuguang Cui