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.

翻译：我们提出了一种交叉注意力Transformer，用于联合解码多个协调接入点接收的上行OFDM信号。一个共享的每接收器编码器学习每个时频网格的结构，一个令牌级交叉注意力模块融合各接收器，为无需显式信道估计的标准信道解码器生成软对数似然比。通过位度量目标进行训练，该模型根据每接收器可靠性调整其融合机制，并在降级链路、强频率选择性和稀疏导频条件下保持鲁棒性。在现实Wi-Fi信道中，它优于传统流水线和强神经网络基线，通常匹配或超越本地完美信道状态信息参考，同时在商用硬件上保持紧凑和计算高效，使其适用于下一代协调Wi-Fi接收器。