Distributionally Robust Wireless Semantic Communication with Large AI Models

• URL: http://arxiv.org/abs/2506.03167v2
• Date: Sun, 02 Nov 2025 00:24:38 GMT
• Title: Distributionally Robust Wireless Semantic Communication with Large AI Models
• Authors: Long Tan Le, Senura Hansaja Wanasekara, Zerun Niu, Nguyen H. Tran, Phuong Vo, Walid Saad, Dusit Niyato, Zhu Han, Choong Seon Hong, H. Vincent Poor,
• Abstract summary: Current SemCom systems fail to generalize across diverse noise conditions, adversarial attacks, and out-of-distribution data.<n>Wasserstein distributionally robust optimization is employed to provide resilience against semantic misinterpretation and channel perturbations.<n> Experimental results on image and text transmission demonstrate that WaSeCom achieves improved robustness under noise and adversarial perturbations.
• Score: 111.47794569742206
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Semantic communication (SemCom) has emerged as a promising paradigm for 6G wireless systems by transmitting task-relevant information rather than raw bits, yet existing approaches remain vulnerable to dual sources of uncertainty: semantic misinterpretation arising from imperfect feature extraction and transmission-level perturbations from channel noise. Current deep learning based SemCom systems typically employ domain-specific architectures that lack robustness guarantees and fail to generalize across diverse noise conditions, adversarial attacks, and out-of-distribution data. In this paper, a novel and generalized semantic communication framework called WaSeCom is proposed to systematically address uncertainty and enhance robustness. In particular, Wasserstein distributionally robust optimization is employed to provide resilience against semantic misinterpretation and channel perturbations. A rigorous theoretical analysis is performed to establish the robust generalization guarantees of the proposed framework. Experimental results on image and text transmission demonstrate that WaSeCom achieves improved robustness under noise and adversarial perturbations. These results highlight its effectiveness in preserving semantic fidelity across varying wireless conditions.

Related papers

• Communication-Efficient Multi-Modal Edge Inference via Uncertainty-Aware Distributed Learning [60.650628083185616]
  We propose a three-stage communication-aware distributed learning framework to improve training and inference efficiency.<n>In StageI, devices perform local multi-modal self-supervised learning to obtain shared and modality-specific encoders without device--server exchange.<n>StageII, distributed fine-tuning with centralized evidential fusion calibrates per-modality uncertainty and reliably aggregates features distorted by noise or channel fading.<n>StageIII, an uncertainty-guided feedback mechanism selectively requests additional features for uncertain samples, optimizing the communication--accuracy tradeoff in the distributed setting.
  arXiv  Detail & Related papers  (2026-01-21T12:38:02Z)
• Prediction-Powered Communication with Distortion Guarantees [65.37485275954224]
  We study a prediction-powered communication setting, in which devices communicate under zero-delay constraints with strict distortion guarantees.<n>We propose two zero-delay compression algorithms leveraging online conformal prediction to provide per-sequence guarantees on the distortion of reconstructed sequences.<n>Experiments on semantic text compression validate the approach, showing significant bit rate reductions.
  arXiv  Detail & Related papers  (2025-09-29T07:19:39Z)
• Generative Feature Imputing - A Technique for Error-resilient Semantic Communication [46.46641562787869]
  This paper proposes a novel framework, termed generative feature imputing, which comprises three key techniques.<n>First, we introduce a spatial error concentration packetization strategy that spatially concentrates feature distortions by encoding feature elements based on their channel mappings.<n>Second, we propose a generative feature imputing method that utilizes a diffusion model to efficiently reconstruct missing features caused by packet losses.<n>Third, we develop a semantic-aware power allocation scheme that enables unequal error protection by allocating transmission power according to the semantic importance of each packet.
  arXiv  Detail & Related papers  (2025-08-25T12:19:48Z)
• 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)
• Diffusion-based Task-oriented Semantic Communications with Model Inversion Attack [6.115539523178243]
  Task-oriented semantic communication is a promising neural network-based system design for 6G networks.<n>We propose a diffusion-based semantic communication framework, named DiffSem, to optimize semantic information reconstruction.<n>Our results show that DiffSem improves the classification accuracy by 10.03%, and maintain stable performance under dynamic channels.
  arXiv  Detail & Related papers  (2025-06-24T05:21:27Z)
• Latent Diffusion Model Based Denoising Receiver for 6G Semantic Communication: From Stochastic Differential Theory to Application [11.385703484113552]
  We propose a novel semantic communication framework empowered by generative artificial intelligence (GAI)<n>A latent diffusion model (LDM)-based semantic communication framework is proposed that combines a variational autoencoder for semantic features extraction.<n>The proposed system is a training-free framework that supports zero-shot generalization, and achieves superior performance under low-SNR and out-of-distribution conditions.
  arXiv  Detail & Related papers  (2025-06-06T03:20:32Z)
• Enhancing Privacy in Semantic Communication over Wiretap Channels leveraging Differential Privacy [51.028047763426265]
  Semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information.<n> transmitting semantic-rich data over insecure channels introduces privacy risks.<n>This paper proposes a novel SemCom framework that integrates differential privacy mechanisms to protect sensitive semantic features.
  arXiv  Detail & Related papers  (2025-04-23T08:42:44Z)
• Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks [55.467288506826755]
  Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks.<n>Most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance.<n>We propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme.
  arXiv  Detail & Related papers  (2025-01-20T04:26:21Z)
• 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)
• Asymmetric Diffusion Based Channel-Adaptive Secure Wireless Semantic Communications [5.539381022630274]
  We propose a secure semantic communication system, DiffuSeC, which leverages the diffusion model and deep reinforcement learning (DRL) With the diffusing module in the sender end and the asymmetric denoising module in the receiver end, the DiffuSeC mitigates the perturbations added by semantic attacks. To further improve the robustness under unstable channel conditions caused by semantic attacks, we developed a DRL-based channel-adaptive diffusion step selection scheme.
  arXiv  Detail & Related papers  (2023-10-30T11:00:47Z)
• A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication [53.78269720999609]
  This paper proposes a semantic communication (SemCom)-empowered AIGC (SemAIGC) generation and transmission framework. Specifically, SemAIGC integrates diffusion models within the semantic encoder and decoder to design a workload-adjustable transceiver. Simulations verify the superiority of our proposed SemAIGC framework in terms of latency and content quality compared to conventional approaches.
  arXiv  Detail & Related papers  (2023-10-26T18:05:22Z)
• Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts [89.04751776308656]
  This paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding. In response to security concerns, we introduce the application of covert communications aided by a friendly jammer.
  arXiv  Detail & Related papers  (2023-09-05T23:24:56Z)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)

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.