Abstract: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.
From: Xavier Tardy [view email]
[v1]
Wed, 4 Feb 2026 16:34:48 UTC (645 KB)
[v2]
Tue, 7 Apr 2026 08:13:04 UTC (969 KB)
[v3]
Mon, 6 Jul 2026 12:10:34 UTC (1,353 KB)
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