Soft Graph Diffusion Transformer for MIMO Detection
Nan Jiang, Jiadong Hong, Lei Liu, Xinyu Bian, Wenjie Wang, Zhaoyang Zhang

TL;DR
The paper introduces SGDiT, a novel flow-based neural network for MIMO detection that models the process as a progressive denoising task, achieving competitive BER performance and good generalization.
Contribution
It proposes a flow matching perspective and a soft graph diffusion transformer with stage-aware information integration for improved neural MIMO detection.
Findings
SGDiT achieves competitive BER performance across various MIMO configurations.
The model generalizes well across different channel conditions.
Using a cross-entropy loss aligns better with the discrete nature of symbol detection.
Abstract
Learning-based MIMO detection has shown strong empirical performance, yet existing methods typically rely on fixed-depth architectures without explicitly modeling the progressive refinement of symbol estimates. In this paper, we revisit MIMO detection from a flow matching perspective and propose the Soft Graph Diffusion Transformer (SGDiT), which reformulates detection as a noise-level-conditioned denoising process that progressively transforms a Gaussian initialization toward the posterior conditioned on channel observations. An adaptive layer normalization (AdaLN)-conditioned soft graph transformer is employed to parameterize the denoising dynamics, enabling stage-aware information integration between observation and symbol domains. To better align with the discrete nature of symbol detection, we further adopt a cross-entropy-based training objective that directly models bit-wise…
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