Latent Diffusion Model Based Denoising Receiver for 6G Semantic Communication: From Stochastic Differential Theory to Application

• URL: http://arxiv.org/abs/2506.05710v1
• Date: Fri, 06 Jun 2025 03:20:32 GMT
• Title: Latent Diffusion Model Based Denoising Receiver for 6G Semantic Communication: From Stochastic Differential Theory to Application
• Authors: Xiucheng Wang, Honggang Jia, Nan Cheng, Dusit Niyato,
• Abstract summary: A novel semantic communication framework empowered by generative artificial intelligence (GAI) is proposed.<n>A theoretical foundation is established based on differential equations (SDEs)<n>A closed-form analytical relationship between the signal-to-noise ratio (SNR) and the denoising timestep is derived.<n>To address the distribution mismatch between the received signal and the DM's training data, a mathematically principled scaling mechanism is introduced.
• Score: 55.42071552739813
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, a novel semantic communication framework empowered by generative artificial intelligence (GAI) is proposed, specifically leveraging the capabilities of diffusion models (DMs). A rigorous theoretical foundation is established based on stochastic differential equations (SDEs), which elucidates the denoising properties of DMs in mitigating additive white Gaussian noise (AWGN) in latent semantic representations. Crucially, a closed-form analytical relationship between the signal-to-noise ratio (SNR) and the denoising timestep is derived, enabling the optimal selection of diffusion parameters for any given channel condition. To address the distribution mismatch between the received signal and the DM's training data, a mathematically principled scaling mechanism is introduced, ensuring robust performance across a wide range of SNRs without requiring model fine-tuning. Built upon this theoretical insight, we develop a latent diffusion model (LDM)-based semantic transceiver, wherein a variational autoencoder (VAE) is employed for efficient semantic compression, and a pretrained DM serves as a universal denoiser. Notably, the proposed architecture is fully training-free at inference time, offering high modularity and compatibility with large-scale pretrained LDMs. This design inherently supports zero-shot generalization and mitigates the challenges posed by out-of-distribution inputs. Extensive experimental evaluations demonstrate that the proposed framework significantly outperforms conventional neural-network-based semantic communication baselines, particularly under low SNR conditions and distributional shifts, thereby establishing a promising direction for GAI-driven robust semantic transmission in future 6G systems.

Related papers

• RIS-aided Latent Space Alignment for Semantic Channel Equalization [10.555901476981923]
  We introduce a new paradigm in wireless communications, focusing on transmitting the intended meaning rather than ensuring strict bit-level accuracy.<n>These systems often rely on Deep Neural Networks (DNNs) to learn and encode meaning directly from data, enabling more efficient communication.<n>In this work, we propose a joint physical and semantic channel equalization framework that leverages the presence of Reconfigurable Intelligent Surfaces (RIS)<n>We show that the proposed joint equalization strategies consistently outperform conventional, disjoint approaches to physical and semantic channel equalization across a broad range of scenarios and wireless channel conditions.
  arXiv  Detail & Related papers  (2025-07-22T10:51:35Z)
• Distributionally Robust Wireless Semantic Communication with Large AI Models [120.29419104482793]
  6G wireless systems are expected to support massive volumes of data with ultra-low latency.<n> conventional bit-level transmission strategies cannot support the efficiency and adaptability required by modern, data-intensive applications.<n>The concept of semantic communication (SemCom) addresses this limitation by focusing on transmitting task-relevant semantic information instead of raw data.
  arXiv  Detail & Related papers  (2025-05-28T04:03:57Z)
• Consistent World Models via Foresight Diffusion [56.45012929930605]
  We argue that a key bottleneck in learning consistent diffusion-based world models lies in the suboptimal predictive ability.<n>We propose Foresight Diffusion (ForeDiff), a diffusion-based world modeling framework that enhances consistency by decoupling condition understanding from target denoising.
  arXiv  Detail & Related papers  (2025-05-22T10:01:59Z)
• Generative Latent Neural PDE Solver using Flow Matching [8.397730500554047]
  We propose a latent diffusion model for PDE simulation that embeds the PDE state in a lower-dimensional latent space.<n>Our framework uses an autoencoder to map different types of meshes onto a unified structured latent grid, capturing complex geometries.<n> Numerical experiments show that the proposed model outperforms several deterministic baselines in both accuracy and long-term stability.
  arXiv  Detail & Related papers  (2025-03-28T16:44:28Z)
• DiffATR: Diffusion-based Generative Modeling for Audio-Text Retrieval [49.076590578101985]
  We present a diffusion-based ATR framework (DiffATR) that generates joint distribution from noise. Experiments on the AudioCaps and Clotho datasets with superior performances, verify the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-09-16T06:33:26Z)
• Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints [66.63250537475973]
  This paper introduces a diffusion-driven semantic communication framework with advanced VAE-based compression for bandwidth-constrained generative model.<n>Our experimental results demonstrate significant improvements in pixel-level metrics like peak signal to noise ratio (PSNR) and semantic metrics like learned perceptual image patch similarity (LPIPS)
  arXiv  Detail & Related papers  (2024-07-26T02:34:25Z)
• Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noises [18.539501941328393]
  This paper develops a latent diffusion model-enabled SemCom system to handle outliers in source data.<n>A lightweight single-layer latent space transformation adapter completes one-shot learning at the transmitter.<n>An end-to-end consistency distillation strategy is used to distill the diffusion models trained in latent space.
  arXiv  Detail & Related papers  (2024-06-09T23:39:31Z)
• Fundamental Limitation of Semantic Communications: Neural Estimation for Rate-Distortion [19.615466425874402]
  This paper studies the fundamental limit of semantic communications over the discrete memoryless channel. We adopt the semantic rate-distortion function (SRDF) to study the relationship among the minimum compression rate, observation distortion, semantic distortion, and channel capacity.
  arXiv  Detail & Related papers  (2024-01-02T12:10:16Z)
• Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications [12.218161437914118]
  conditional denoising diffusion probabilistic models (DDPMs) are proposed to enhance the data transmission and reconstruction over wireless channels. Inspired by this, the key idea is to leverage the generative prior of diffusion models in learning a "noisy-to-clean" transformation of the information signal. The proposed scheme could be beneficial for communication scenarios in which a prior knowledge of the information content is available.
  arXiv  Detail & Related papers  (2023-10-30T11:33:01Z)
• Adversarial Training of Denoising Diffusion Model Using Dual Discriminators for High-Fidelity Multi-Speaker TTS [0.0]
  The diffusion model is capable of generating high-quality data through a probabilistic approach. It suffers from the drawback of slow generation speed due to the requirement of a large number of time steps. We propose a speech synthesis model with two discriminators: a diffusion discriminator for learning the distribution of the reverse process and a spectrogram discriminator for learning the distribution of the generated data.
  arXiv  Detail & Related papers  (2023-08-03T07:22:04Z)
• PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion [66.95761172711073]
  generalization of neural networks is a central challenge in machine learning. We propose to enhance it directly through the underlying function of neural networks, rather than focusing on adjusting input data. We put this theoretical framework into practice as $textbfPDE+$ ($textbfPDE$ with $textbfA$daptive $textbfD$istributional $textbfD$iffusion)
  arXiv  Detail & Related papers  (2023-05-25T08:23:26Z)
• Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme [17.4244108919728]
  To serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become pivotal issue in semantic communications. This paper proposes a deep separate source-channel coding framework for the joint task and data oriented semantic communications. An iterative training algorithm is proposed to tackle the overfitting issue of deep learning models.
  arXiv  Detail & Related papers  (2023-02-27T08:34:37Z)
• Semantic Image Synthesis via Diffusion Models [174.24523061460704]
  Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable success in various image generation tasks.<n>Recent work on semantic image synthesis mainly follows the de facto GAN-based approaches.<n>We propose a novel framework based on DDPM for semantic image synthesis.
  arXiv  Detail & Related papers  (2022-06-30T18:31:51Z)
• Influence Estimation and Maximization via Neural Mean-Field Dynamics [60.91291234832546]
  We propose a novel learning framework using neural mean-field (NMF) dynamics for inference and estimation problems. Our framework can simultaneously learn the structure of the diffusion network and the evolution of node infection probabilities.
  arXiv  Detail & Related papers  (2021-06-03T00:02:05Z)
• Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
  We propose a novel learning framework for inference and estimation problems of diffusion on networks. Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities. Our approach is versatile and robust to variations of the underlying diffusion network models.
  arXiv  Detail & Related papers  (2020-06-16T18:45:20Z)
• Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees [49.91477656517431]
  Quantization-based solvers have been widely adopted in Federated Learning (FL) No existing methods enjoy all the aforementioned properties. We propose an intuitively-simple yet theoretically-simple method based on SIGNSGD to bridge the gap.
  arXiv  Detail & Related papers  (2020-02-25T15:12:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.