A causal diffusion model reconstructs videos from ultra-low-bitrate semantics and compressed frames using temporal distillation from a bidirectional teacher, outperforming prior baselines.
Generative AI meets semantic communication: Evolution and revolution of communication tasks
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Q-GESCO uses quantized diffusion models to regenerate images from semantic maps in noisy channels, matching full-precision performance with up to 75% memory and 79% FLOP reductions.
Introduces a null-space diffusion sampling method for training-free multi-user generative semantic communications in OFDMA systems.
The tutorial synthesizes diffusion model techniques for generative semantic communications to achieve high compression while preserving meaning in wireless transmission.
citing papers explorer
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A Causal Diffusion Model for Video Reconstruction from Ultra-Low-Bitrate Representations
A causal diffusion model reconstructs videos from ultra-low-bitrate semantics and compressed frames using temporal distillation from a bidirectional teacher, outperforming prior baselines.
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Lightweight Diffusion Models for Resource-Constrained Semantic Communication
Q-GESCO uses quantized diffusion models to regenerate images from semantic maps in noisy channels, matching full-precision performance with up to 75% memory and 79% FLOP reductions.
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Training-Free Multi-User Generative Semantic Communications via Null-Space Diffusion Sampling
Introduces a null-space diffusion sampling method for training-free multi-user generative semantic communications in OFDMA systems.
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Generative AI Meets 6G and Beyond: Diffusion Models for Semantic Communications
The tutorial synthesizes diffusion model techniques for generative semantic communications to achieve high compression while preserving meaning in wireless transmission.