Overview
- Field: Machine Learning
- Authors: Yubo Zhang, Yiyao Liu, Xiaodong Wang
- Published: 2026-08-17
- arXiv: 2508.08539
Summary
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 trained hard policy is cloned at the parameter level to 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 allowing layer- and round-specific specialization under decoder feedback.
Within each IDD round, decoder priors bias 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 progressively exposes the receiver to synthetic and in-the-loop decoder-generated priors, further stabilizing the hard-to-soft transfer.
*Auto-collected on 2026-08-18.*