arXiv:2602. 04728v3 Announce Type: replace-cross Abstract: We propose a cross-attention Transformer for joint decoding of uplink OFDM signals received by multiple coordinated access points.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2601. 17108v2 Announce Type: replace-cross Abstract: This paper proposes a hybrid Mamba-attention neural architecture to achieve improved channel estimation for orthogonal frequency-division multiplexing (OFDM) waveforms, particularly for configurations with a large number of subcarriers.
arXiv:2506. 00452v5 Announce Type: replace-cross Abstract: In orthogonal frequency division multiplexing (OFDM), accurate channel estimation is crucial.
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.
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:2608. 08439v1 Announce Type: cross Abstract: Heterogeneous RF sensing differs substantially in feature structure, spatial layout, and temporal scale, making existing models difficult to reuse across devices, environments, and RF modalities.
arXiv:2606. 11857v1 Announce Type: cross Abstract: Multi-channel mixed-SNR training improves out-of-distribution (OOD) generalisation of deep learning channel estimators for IEEE 802.