Learning-to-Transition for Large-scale and High-Order MIMO Detection

Modern wireless systems need to decode signals sent simultaneously from many antennas, a computationally brutal search problem especially at high signal orders. This paper reframes MIMO detection as a learned sequential decision process, using a Transformer to guide symbol-by-symbol search while producing reliable confidence scores for downstream error correction. A clever "hard-to-soft" training trick transfers a well-trained detector into a full iterative decoding pipeline. Applications span 5G/6G wireless infrastructure, massive MIMO systems, and any communications hardware needing fast, accurate, learnable alternatives to traditional signal detection algorithms. Authors: Yubo Zhang, Yiyao Liu, Xiaodong Wang Paper: https://arxiv.org/abs/2608.14511v1