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 time-frequency structure within each received grid, and a token-wise cross-attention module fuses the receivers to produce soft log-likelihood ratios for a standard channel decoder, without requiring explicit per-receiver channel estimates. Trained with a bit-metric objective, the model adapts its fusion to per-receiver reliability, tolerates missing or degraded links, and remains robust when pilots are sparse. Across realistic Wi-Fi channels, it consistently outperforms classical pipelines and strong convolutional baselines, frequently matching (and in some cases surpassing) a powerful baseline that assumes perfect channel knowledge per access point. Despite its expressiveness, the architecture is compact, has low computational cost (low GFLOPs), and achieves low latency on GPUs, making it a practical building block for next-generation Wi-Fi receivers.

翻译：我们提出了一种交叉注意力Transformer模型，用于对多个协调接入点接收的上行链路OFDM信号进行联合解码。该模型采用共享的每接收机编码器学习各接收网格内的时频结构，并通过令牌级交叉注意力模块融合多接收机信息，为标准信道解码器生成软对数似然比，无需显式的每接收机信道估计。通过以比特度量目标进行训练，该模型能够根据每接收机可靠性自适应调整融合策略，容忍链路缺失或劣化，并在导频稀疏时保持鲁棒性。在真实Wi-Fi信道环境下，其性能持续优于经典处理流程与强卷积基线，且频繁达到（部分情况下甚至超越）假设各接入点具备完美信道知识的强基线水平。尽管表达能力强大，该架构结构紧凑、计算成本低（GFLOPs少），并在GPU上实现低延迟，可成为下一代Wi-Fi接收机的实用构建模块。