Neural networks can learn efficient search strategies for MIMO detection that match or exceed traditional algorithms, and these learned policies can be transferred to soft-output receivers that improve when combined with iterative decoding.
This paper presents a learning-to-transition framework that uses Transformers and neural networks to efficiently detect high-order MIMO signals. The approach treats detection as a sequence of symbol transitions, learns to search the discrete symbol space effectively, and integrates with channel decoding through an iterative receiver that adapts based on decoder feedback.