这篇论文提出L2T框架，用Transformer做MIMO检测的转移策略，硬输出和软输出都覆盖，挺有新意。
该论文提出L2T框架，将MIMO检测建模为完整向量转移的随机序列。每个转移步骤中，信道耦合的Transformer更新实例嵌入与采样策略，并通过分块自回归分解捕获跨流依赖。硬输出检测采用递归转移网络，通过残差到BER课程训练，先学习精确残差度量的搜索几何，再对齐策略与传输比特准确率。软输出接收则通过参数级克隆，将训练好的硬策略应用于未绑定软输入软输出IDD接收机，并在LDPC解码中产生后验与外部对数似然比。多阶段训练策略稳定了硬到软的迁移。
Learning-to-Transition for Large-scale and High-Order MIMO Detection
High-order multiple-input multiple-output (MIMO) detection requires efficient search over a large discrete symbol space while producing reliable soft information for channel decoding. This paper develops a learning-to-transition (L2T) framework that formulates MIMO detection as a stochastic sequence of complete-vector transitions. At each transition, a channel-coupled Transformer updates both the instance embedding and the sampling policy, while a blockwise autoregressive factorization captures inter-stream dependence with moderate sequential complexity. For hard-output detection, a transition network is applied recursively and trained through a residual-to-BER curriculum, which first learns the MIMO search geometry from the exact residual metric and then aligns the policy with transmitted-bit accuracy. For soft-output reception, the well-trained hard policy is cloned at the parameter level into every layer of an untied soft-input soft-output iterative detection and decoding (IDD) receiver. This tied-to-untied transfer preserves the learned zero-prior search dynamics while enabling layer- and round-specific specialization under decoder feedback. Within each IDD round, decoder priors tilt candidate generation according to Bayes' rule, and likelihood-weighted terminal hypotheses produce posterior and extrinsic log-likelihood ratios for LDPC decoding. A multi-stage training strategy further stabilizes the hard-to-soft transfer by progressively exposing the receiver to synthetic and in-loop decoder-generated priors.