Soft Graph Diffusion Transformer for MIMO Detection
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 inte...
| Authors | Nan Jiang, Jiadong Hong, Lei Liu et al. |
|---|---|
| Primary category | cs.IT |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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