The work characterizes the scalar Gaussian Rényi rate-distortion-perception-privacy tradeoff under indirect observation and introduces a conditional privacy measure that avoids penalizing legitimate semantic recovery.
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Semantic communications: Principles and challenges
13 Pith papers cite this work. Polarity classification is still indexing.
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Semantic rate-distortion theory shows that under closure fidelity the rate-distortion function depends only on the irredundant core, yielding zero-distortion rates strictly below classical entropy and semantic leverage in source-channel coding.
Semantic channel theory achieves deductive compression where minimum block length under closure fidelity depends on the irredundant semantic core size rather than full knowledge-base size.
A generative semantic communication system that sends compressed semantic information and uses diffusion models with spatially-adaptive normalizations to reconstruct high-quality, semantically consistent images even under severe channel noise.
Neural Pub/Sub uses a MAPE-K loop with Walrasian price signals on service DAGs to achieve autonomic federated orchestration that matches centralized welfare under gross-substitutes assumptions and outperforms baselines in small-scale experiments.
Resolution information equals a binary divergence based only on prior probabilities when posteriors are unconstrained, but constrained generative representations can induce irreducible ambiguity floors due to posterior geometry.
ChronoSC projects video temporal dynamics into a compact chrono-image via color stacking, transmits it with lightweight DeepJSCC, reconstructs explicitly, and applies a pre-trained BLIP model for VideoQA answers, delivering 192x bandwidth savings on CLEVRER.
ADDPS formulates semantic decoding as a Bayesian inverse problem and uses alternating latent- and image-domain consistency enforcement during diffusion sampling to achieve optimal perceptual quality by preserving the data distribution.
An intention-aware semantic agent system for AI glasses reduces bandwidth by over 50% in simulations while preserving task performance through adaptive preprocessing guided by inferred user intentions.
WirelessAgent uses AI to predict missing CSI and an agent on Coze for natural-language multi-objective wireless resource allocation, cutting RMSE by up to 67% in simulations.
An O-A-R model driven adaptive hierarchical transmission system for multimodal semantic communication achieves over 90% bandwidth savings at 1-3 kbps and eliminates cliff effects in deep fading channels by sending decision-oriented semantic graphs rather than pixels.
Introduces a null-space diffusion sampling method for training-free multi-user generative semantic communications in OFDMA systems.
LPGF fuses Sentinel-2 imagery, UAV telemetry, vehicle GPS, and OSM data into building height priors via a three-tier hierarchy and optional quality-gated shadow estimation, reporting MAE of 3.07 m on Milan validation.
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Generative Semantic Communication: Diffusion Models Beyond Bit Recovery
A generative semantic communication system that sends compressed semantic information and uses diffusion models with spatially-adaptive normalizations to reconstruct high-quality, semantically consistent images even under severe channel noise.