arXiv:2506. 00452v5 Announce Type: replace-cross Abstract: In orthogonal frequency division multiplexing (OFDM), accurate channel estimation is crucial.
By TaeJun Ha, Chaehyun Jung, Hyeonuk Kim, Jeongwoo Park, Jeonghun Park
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.
By Xavier Tardy, Gr\'egoire Lefebvre, Apostolos Kountouris, Ha\"ifa Fares, Amor Nafkha
MambaCSP is a hybrid-attention state space model that replaces transformer-based backbones with a linear-time Mamba architecture for channel state prediction. By adding lightweight patch‑mixer attention layers, it captures long‑range dependencies while maintaining hardware efficiency. Experiments on MISO‑OFDM show 9‑12% higher accuracy, 3× faster throughput, 2.6× lower VRAM usage, and 2.9× faster inference compared to LLM‑based methods.
By Aladin Djuhera, Haris Gacanin, Holger Boche
arXiv:2605.10681v2 Announce Type: replace-cross
Abstract: Forward error correction is essential for reliable communication over noisy channels. Attention-based model-free neural decoders have shown s...
By Rostislav Gusev, Nikita Aleksandrov, Artem Solomkin, Dmitry Artemasov
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:2605.00020v2 Announce Type: replace-cross
Abstract: The success of large foundation models is catalyzing a new paradigm for AI-native 6G network design: wireless foundation models for physical-...
By Kejia Bian, Meixia Tao, Jianhua Mo, Zhiyong Chen, Leyan Chen