Conditional Denoising Diffusion Autoencoders for Wireless Semantic Communications
- URL: http://arxiv.org/abs/2509.22282v1
- Date: Fri, 26 Sep 2025 12:46:21 GMT
- Title: Conditional Denoising Diffusion Autoencoders for Wireless Semantic Communications
- Authors: Mehdi Letafati, Samad Ali, Matti Latva-aho,
- Abstract summary: A wireless SemCom system aims to learn the mapping from low-dimensional semantics to high-dimensional ground-truth.<n>A neural encoder at semantic transmitter extracts the high-level semantics.<n>A conditional diffusion model (CDiff) at the semantic receiver exploits the source distribution for signal-space denoising.<n>It is analytically proved that the proposed decoder model is a consistent estimator of the ground-truth data.
- Score: 10.896931510442514
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Semantic communication (SemCom) systems aim to learn the mapping from low-dimensional semantics to high-dimensional ground-truth. While this is more akin to a "domain translation" problem, existing frameworks typically emphasize on channel-adaptive neural encoding-decoding schemes, lacking full exploration of signal distribution. Moreover, such methods so far have employed autoencoder-based architectures, where the encoding is tightly coupled to a matched decoder, causing scalability issues in practice. To address these gaps, diffusion autoencoder models are proposed for wireless SemCom. The goal is to learn a "semantic-to-clean" mapping, from the semantic space to the ground-truth probability distribution. A neural encoder at semantic transmitter extracts the high-level semantics, and a conditional diffusion model (CDiff) at the semantic receiver exploits the source distribution for signal-space denoising, while the received semantic latents are incorporated as the conditioning input to "steer" the decoding process towards the semantics intended by the transmitter. It is analytically proved that the proposed decoder model is a consistent estimator of the ground-truth data. Furthermore, extensive simulations over CIFAR-10 and MNIST datasets are provided along with design insights, highlighting the performance compared to legacy autoencoders and variational autoencoders (VAE). Simulations are further extended to the multi-user SemCom, identifying the dominating factors in a more realistic setup.
Related papers
- Channel-Aware Vector Quantization for Robust Semantic Communication on Discrete Channels [5.680520767606761]
We propose a channel-aware vector quantization (CAVQ) algorithm within a joint source-channel coding framework, termed VQJSCC.<n>In this framework, semantic features are discretized and directly mapped to modulation constellation symbols, while CAVQ integrates channel transition probabilities into the quantization process.<n>A multi-codebook alignment mechanism is also introduced to handle mismatches between codebook order and modulation order by decomposing the transmission stream into subchannels.
arXiv Detail & Related papers (2025-10-21T13:02:35Z) - Semantic Channel Equalization Strategies for Deep Joint Source-Channel Coding [8.967618587731694]
Deep joint source-channel coding (DeepJSCC) has emerged as a powerful paradigm for end-to-end semantic communications.<n>Existing DeepJSCC schemes assume a shared latent space at transmitter (TX) and receiver (RX)<n>This mismatch introduces "semantic noise", degrading reconstruction quality and downstream task performance.
arXiv Detail & Related papers (2025-10-06T10:29:07Z) - 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) - Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
We propose a novel agent-driven generative semantic communication framework based on reinforcement learning.
In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling.
The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
arXiv Detail & Related papers (2024-04-10T13:24:27Z) - Joint Sensing and Semantic Communications with Multi-Task Deep Learning [45.622060532244944]
This paper explores the integration of deep learning techniques for joint sensing and communications, with an extension to semantic communications.
The integrated system comprises a transmitter and receiver operating over a wireless channel, subject to noise and fading.
The transmitter employs a deep neural network (DNN), namely an encoder, for joint operations of source coding, channel coding, and modulation.
The receiver utilizes another DNN, namely a decoder, for joint operations of demodulation, channel decoding, and source decoding to reconstruct the data samples.
arXiv Detail & Related papers (2023-11-08T21:03:43Z) - Semantics Alignment via Split Learning for Resilient Multi-User Semantic
Communication [56.54422521327698]
Recent studies on semantic communication rely on neural network (NN) based transceivers such as deep joint source and channel coding (DeepJSCC)
Unlike traditional transceivers, these neural transceivers are trainable using actual source data and channels, enabling them to extract and communicate semantics.
We propose a distributed learning based solution, which leverages split learning (SL) and partial NN fine-tuning techniques.
arXiv Detail & Related papers (2023-10-13T20:29:55Z) - Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial
Attacks [70.51799606279883]
We introduce test-time adversarial attacks on deep neural networks (DNNs) for semantic communications.
We show that it is possible to change the semantics of the transferred information even when the reconstruction loss remains low.
arXiv Detail & Related papers (2022-12-20T17:13:22Z) - Denoising Diffusion Error Correction Codes [92.10654749898927]
Recently, neural decoders have demonstrated their advantage over classical decoding techniques.
Recent state-of-the-art neural decoders suffer from high complexity and lack the important iterative scheme characteristic of many legacy decoders.
We propose to employ denoising diffusion models for the soft decoding of linear codes at arbitrary block lengths.
arXiv Detail & Related papers (2022-09-16T11:00:50Z) - Model-based Deep Learning Receiver Design for Rate-Splitting Multiple
Access [65.21117658030235]
This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods.
The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead.
Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
arXiv Detail & Related papers (2022-05-02T12:23:55Z)
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.