arXiv AI
Echo State Network (ESN) for Signal Recovery in RF-Impaired IBFD MIMO Systems
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arXiv Machine Learning
Oct 9
SR-TTA: Spatial-Redundancy Test-Time Adaptation for Interference-Robust Respiration Sensing
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.
arXiv Machine Learning
Aug 7
EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver
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.
arXiv Machine Learning
Jul 10
Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems
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.
arXiv Machine Learning
Jul 31
Neural Network-Assisted CLEAN for Channel Modeling in Low-SNR Regimes
arXiv:2607. 27450v1 Announce Type: new Abstract: Accurate multipath parameter estimation is critical for modern wireless communication systems, particularly in challenging low-SNR environments.
arXiv Machine Learning
5d ago
XL MIMO Extreme Learning Machines: Wave-Domain Inference with Stacked Metasurfaces
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 AI
5d ago
In-Context Learning for MIMO Turbo Receivers
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.