Null-space flow matching decomposes MIMO CSI estimation into direct range-space reconstruction from noisy pilots and iterative FM-based null-space generation, achieving competitive NMSE under a ~3 ms latency budget with a power-law schedule and noise-aware correction.
Diffusion- based generative prior for low-complexity MIMO channel estimation,
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Tensor-train low-rank structure enables tractable near-optimal Bayesian inference for high-dimensional MIMO detection and soft-decision decoding.
A mixture-of-experts diffusion model with variational Bayesian inference jointly infers the channel and expert indicator to adapt to different propagation environments in massive MIMO channel estimation.
citing papers explorer
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Null-Space Flow Matching for MIMO Channel Estimation in Latency-Constrained Systems
Null-space flow matching decomposes MIMO CSI estimation into direct range-space reconstruction from noisy pilots and iterative FM-based null-space generation, achieving competitive NMSE under a ~3 ms latency budget with a power-law schedule and noise-aware correction.
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A Tensor-Train Framework for Bayesian Inference in High-Dimensional Systems: Applications to MIMO Detection and Channel Decoding
Tensor-train low-rank structure enables tractable near-optimal Bayesian inference for high-dimensional MIMO detection and soft-decision decoding.
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Mixture-of-Experts Diffusion Models for Adaptive Massive MIMO Channel Estimation via Variational Bayesian Inference
A mixture-of-experts diffusion model with variational Bayesian inference jointly infers the channel and expert indicator to adapt to different propagation environments in massive MIMO channel estimation.