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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2602.04728 |
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| _version_ | 1866908940463243264 |
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| author | Tardy, Xavier Lefebvre, Grégoire Kountouris, Apostolos Fares, Haïfa Nafkha, Amor |
| author_facet | Tardy, Xavier Lefebvre, Grégoire Kountouris, Apostolos Fares, Haïfa Nafkha, Amor |
| contents | We propose a cross-attention Transformer for joint decoding of uplink OFDM signals received by multiple coordinated access points. A shared per-receiver encoder learns the time-frequency structure of each grid, and a token-wise cross-attention module fuses the receivers to produce soft log-likelihood ratios for a standard channel decoder without explicit channel estimates. Trained with a bit-metric objective, the model adapts its fusion to per-receiver reliability and remains robust under degraded links, strong frequency selectivity, and sparse pilots. Over realistic Wi-Fi channels, it outperforms classical pipelines and strong neural baselines, often matching or surpassing a local perfect-CSI reference while remaining compact and computationally efficient on commodity hardware, making it suitable for next-generation coordinated Wi-Fi receivers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04728 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scalable Cross-Attention Transformer for Cooperative Multi-AP OFDM Uplink Reception Tardy, Xavier Lefebvre, Grégoire Kountouris, Apostolos Fares, Haïfa Nafkha, Amor Signal Processing Information Theory Machine Learning 94A12, 68T05 I.2.6; C.2.1 We propose a cross-attention Transformer for joint decoding of uplink OFDM signals received by multiple coordinated access points. A shared per-receiver encoder learns the time-frequency structure of each grid, and a token-wise cross-attention module fuses the receivers to produce soft log-likelihood ratios for a standard channel decoder without explicit channel estimates. Trained with a bit-metric objective, the model adapts its fusion to per-receiver reliability and remains robust under degraded links, strong frequency selectivity, and sparse pilots. Over realistic Wi-Fi channels, it outperforms classical pipelines and strong neural baselines, often matching or surpassing a local perfect-CSI reference while remaining compact and computationally efficient on commodity hardware, making it suitable for next-generation coordinated Wi-Fi receivers. |
| title | Scalable Cross-Attention Transformer for Cooperative Multi-AP OFDM Uplink Reception |
| topic | Signal Processing Information Theory Machine Learning 94A12, 68T05 I.2.6; C.2.1 |
| url | https://arxiv.org/abs/2602.04728 |