A Conditional Diffusion Transformer recovers full MIMO-OFDM channels from sparse noisy pilots, delivering over 5 dB gain versus baselines even at 1/32 pilot density and completing inference in 10 steps.
Diffusion models for wireless transceivers: From pilot-efficient channel estimation to AI-native 6G receivers
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Telecom World Models introduce a three-layer architecture for learned, action-conditioned, uncertainty-aware modeling of 6G network dynamics, combining digital twins and foundation models, with a network slicing proof-of-concept showing improved KPI prediction over baselines.
An incrementally conditioned diffusion receiver with in-loop post-training and soft decision feedback improves MIMO-OFDM channel estimation and symbol detection, working down to 1/32 pilot density.
citing papers explorer
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Diffusion Inpainting MIMO-OFDM Channels with Limited Noisy Observations
A Conditional Diffusion Transformer recovers full MIMO-OFDM channels from sparse noisy pilots, delivering over 5 dB gain versus baselines even at 1/32 pilot density and completing inference in 10 steps.
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Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G
Telecom World Models introduce a three-layer architecture for learned, action-conditioned, uncertainty-aware modeling of 6G network dynamics, combining digital twins and foundation models, with a network slicing proof-of-concept showing improved KPI prediction over baselines.
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MIMO-OFDM AI Receiver Based on Incrementally Conditioned Diffusion with Soft Decision
An incrementally conditioned diffusion receiver with in-loop post-training and soft decision feedback improves MIMO-OFDM channel estimation and symbol detection, working down to 1/32 pilot density.