面向大规模高阶MIMO检测的学习式转换（L2T）
Learning-to-Transition for Large-scale and High-Order MIMO Detection
浏览论文内容
中文总结 AI 辅助
本文提出学习式转换（L2T）框架，将MIMO检测建模为完整向量转换的随机序列，结合信道耦合Transformer与分块自回归因子化，通过多阶段训练实现硬到软输出的转换，提升大规模高阶MIMO检测性能。
中文摘要 AI 辅助
高阶多输入多输出（MIMO）检测需要在庞大的离散符号空间中进行高效搜索，同时为信道解码生成可靠的软信息。本文提出一种学习式转换（L2T）框架，将MIMO检测建模为完整向量转换的随机序列。在每次转换时，信道耦合Transformer会同时更新实例嵌入和采样策略，而分块自回归因子化则以适中的序列复杂度捕捉流间依赖关系。对于硬输出检测，转换网络会递归应用，并通过残差到误码率（BER）的课程进行训练，该课程首先从精确残差度量中学习MIMO搜索几何，随后使策略与传输比特精度对齐。对于软输出接收，训练好的硬策略会在参数层面克隆到非绑定软输入软输出迭代检测与解码（IDD）接收器的每一层，这种从绑定到非绑定的转换保留了所学的零先验搜索动态，同时支持在解码器反馈下的层级和轮级专门化。在每个IDD轮次中，解码器先验会根据贝叶斯规则调整候选生成，而似然加权的终端假设会为LDPC解码生成后验和外部对数似然比。多阶段训练策略通过逐步让接收器接触合成及循环内解码器生成的先验，进一步稳定硬到软的转换。
英文摘要
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.
