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
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- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
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- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
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- LinkedLinked via arxiv author · 85%Yubo Zhang →
“Learning-to-Transition for Large-scale and High-Order MIMO Detection”
- LinkedLinked via arxiv author · 85%Yiyao Liu →
“Learning-to-Transition for Large-scale and High-Order MIMO Detection”
- LinkedLinked via arxiv author · 85%Xiaodong Wang →
“Learning-to-Transition for Large-scale and High-Order MIMO Detection”