用跨注意力机制联合解码多接入点的上行OFDM信号，提升无线网络性能。
Scalable Cross-Attention Transformer for Cooperative Multi-AP OFDM Uplink Reception
- 通过共享编码器和逐标记跨注意力融合多接收端信息
- 在真实Wi-Fi信道下超越经典方法和强神经基线，接近理想信道估计效果
- 模型轻量高效，适合部署在普通硬件上的下一代协同Wi-Fi接收机
我们提出一种用于多个协调接入点联合解码上行OFDM信号的跨注意力Transformer。每个接收端使用共享的编码器学习其时频网格结构，再通过逐标记的跨注意力模块融合各接收端信息，生成标准信道译码器所需的软对数似然比，无需显式信道估计。模型采用比特级目标函数训练，能自适应各接收端的可靠性，在链路质量下降、强频率选择性及稀疏导频条件下仍保持鲁棒性。在真实Wi-Fi信道上，该模型显著优于经典处理流程和强神经基线，常达到或超过本地理想信道状态信息（perfect-CSI）参考性能，同时保持模型紧凑且计算高效，适用于下一代协同Wi-Fi接收机在商用硬件上的部署。
原文摘要 · Abstract (English)
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.
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