Title: Soft Graph Transformer for MIMO Detection Show Chinese title

Title: 软图变换器用于MIMO检测

Abstract: 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. Show Chinese abstract

Abstract: 我们提出了软图变换器（SGT），一种软输入软输出的神经架构，专为MIMO检测设计。 虽然最大似然（ML）检测能够实现最佳准确性，但其指数级的复杂度使其在大型系统中不可行，而传统的消息传递算法依赖于渐近假设，这在有限维度中通常失效。 最近的基于Transformer的检测器表现出强大的性能，但通常忽略了MIMO因子图结构，无法利用先验软信息。 SGT通过结合自注意力（编码符号和约束子图内的上下文依赖关系）与图感知交叉注意力（在子图之间进行结构化消息传递）来解决这些限制。 其软输入接口允许集成辅助先验信息，在保持计算效率的同时生成有效的软输出。 实验表明，SGT实现了接近ML的性能，并为利用软先验的接收机系统提供了一个灵活且可解释的框架。