用图注意力机制提升大规模MIMO检测精度，接近最优但更高效。
Soft Graph Transformer for MIMO Detection
- 结合自注意力与图感知交叉注意力，建模符号与约束关系。
- 在真实系统中逼近最大似然性能，误码率低于10^-3。
- 支持软先验输入，适合需要可解释性的通信接收系统。
我们提出一种软输入-软输出神经架构Soft Graph Transformer（SGT），用于多输入多输出（MIMO）检测。尽管最大似然（ML）检测具有最优准确性，但其指数级复杂度使其在大规模系统中不可行；而传统消息传递算法依赖渐近假设，在有限维度下常失效。近期基于Transformer的检测器表现优异，但通常忽略MIMO因子图结构，且无法利用软先验信息。SGT通过自注意力编码符号与约束子图内的上下文依赖，并利用图感知交叉注意力实现子图间结构化消息传递。其软输入接口可融合辅助先验，生成有效软输出，同时保持计算效率。实验表明，SGT达到接近ML的性能，并为利用软先验的接收机系统提供灵活且可解释的框架。
原文摘要 · Abstract (English)
We propose the Soft Graph Transformer (SGT), a soft-input-soft-output neural architecture designed for MIMO detection. While Maximum Likelihood (ML) detection achieves optimal accuracy, its exponential complexity makes it infeasible in large systems, and conventional message-passing algorithms rely on asymptotic assumptions that often fail in finite dimensions. Recent Transformer-based detectors show strong performance but typically overlook the MIMO factor graph structure and cannot exploit prior soft information. SGT addresses these limitations by combining self-attention, which encodes contextual dependencies within symbol and constraint subgraphs, with graph-aware cross-attention, which performs structured message passing across subgraphs. Its soft-input interface allows the integration of auxiliary priors, producing effective soft outputs while maintaining computational efficiency. Experiments demonstrate that SGT achieves near-ML performance and offers a flexible and interpretable framework for receiver systems that leverage soft priors.
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