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Generative AI-Enhanced Multi-Modal Semantic Communication in Internet of Vehicles: System Design and Methodologies

Jiayi Lu, Wanting Yang, Zehui Xiong, Chengwen Xing, Rahim Tafazolli, Tony Q. S. Quek, Merouane Debbah

TL;DR

The paper tackles the challenge of efficient, reliable multi-modal semantic communication in highly dynamic IoV networks. It introduces G-MSC, a Generative AI-enhanced SemCom framework that optimizes semantic encoding, channel modeling/estimation, and decoding while supporting both analog and digital transmission modes. A case study on predictive BEV tasks demonstrates that diffusion-model refinement significantly improves IoU and visual quality, and enables forward-looking BEV predictions for multiple future frames. The findings illustrate the potential of integrating GAI into SemCom to reduce data transmission while preserving semantic integrity, with promising directions toward hybrid transmission, semantic scheduling, and cross-task coordination.

Abstract

Vehicle-to-everything (V2X) communication supports numerous tasks, from driving safety to entertainment services. To achieve a holistic view, vehicles are typically equipped with multiple sensors to compensate for undetectable blind spots. However, processing large volumes of multi-modal data increases transmission load, while the dynamic nature of vehicular networks adds to transmission instability. To address these challenges, we propose a novel framework, Generative Artificial intelligence (GAI)-enhanced multi-modal semantic communication (SemCom), referred to as G-MSC, designed to handle various vehicular network tasks by employing suitable analog or digital transmission. GAI presents a promising opportunity to transform the SemCom framework by significantly enhancing semantic encoding to facilitate the optimized integration of multi-modal information, enhancing channel robustness, and fortifying semantic decoding against noise interference. To validate the effectiveness of the G-MSC framework, we conduct a case study showcasing its performance in vehicular communication networks for predictive tasks. The experimental results show that the design achieves reliable and efficient communication in V2X networks. In the end, we present future research directions on G-MSC.

Generative AI-Enhanced Multi-Modal Semantic Communication in Internet of Vehicles: System Design and Methodologies

TL;DR

The paper tackles the challenge of efficient, reliable multi-modal semantic communication in highly dynamic IoV networks. It introduces G-MSC, a Generative AI-enhanced SemCom framework that optimizes semantic encoding, channel modeling/estimation, and decoding while supporting both analog and digital transmission modes. A case study on predictive BEV tasks demonstrates that diffusion-model refinement significantly improves IoU and visual quality, and enables forward-looking BEV predictions for multiple future frames. The findings illustrate the potential of integrating GAI into SemCom to reduce data transmission while preserving semantic integrity, with promising directions toward hybrid transmission, semantic scheduling, and cross-task coordination.

Abstract

Vehicle-to-everything (V2X) communication supports numerous tasks, from driving safety to entertainment services. To achieve a holistic view, vehicles are typically equipped with multiple sensors to compensate for undetectable blind spots. However, processing large volumes of multi-modal data increases transmission load, while the dynamic nature of vehicular networks adds to transmission instability. To address these challenges, we propose a novel framework, Generative Artificial intelligence (GAI)-enhanced multi-modal semantic communication (SemCom), referred to as G-MSC, designed to handle various vehicular network tasks by employing suitable analog or digital transmission. GAI presents a promising opportunity to transform the SemCom framework by significantly enhancing semantic encoding to facilitate the optimized integration of multi-modal information, enhancing channel robustness, and fortifying semantic decoding against noise interference. To validate the effectiveness of the G-MSC framework, we conduct a case study showcasing its performance in vehicular communication networks for predictive tasks. The experimental results show that the design achieves reliable and efficient communication in V2X networks. In the end, we present future research directions on G-MSC.
Paper Structure (24 sections, 4 figures, 3 tables)

This paper contains 24 sections, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Communication-Dependent Tasks in IoV
  3. Typical Communication Tasks
  4. Typical Communication Data Modalities
  5. Challenges and Solutions
  6. Framework Design of G-MSC
  7. GAI-Powered Multi-Modal Semantic Encoder
  8. Analog encoding
  9. Digital encoding
  10. GAI-Powered High-Mobility Wireless Transmission
  11. GAI-enhanced channel modeling
  12. GAI-enhanced channel estimation
  13. GAI-enhanced multiple access
  14. GAI-Powered Multi-Task Semantic Decoder
  15. Analog decoding
  16. ...and 9 more sections

Figures (4)

  • Figure 1: The three levels of information theory communication. Compared to semantic-level communication, which focuses on accurately conveying comprehensive semantic information embedded in source data, effectiveness-level communication, only focuses on transmitting the task-specific semantic information required by different vehicles in IoV.
  • Figure 2: A realistic Internet of Vehicles scenario with multi-modal V2X communication.
  • Figure 4: Multi-modal fusion methods in vehicular networks. The top orange line is for projecting radar point cloud data to camera dimensions for fusion, the bottom blue line is for projecting camera image data to radar point cloud dimensions for fusion, and the middle is for separate processing and BEV fusion of both modalities.
  • Figure 5: Experimental results of diffusion model-enhanced BEV fusion for image generation and prediction tasks. The lines for lossless channel transmission and MSC transmission without DM are represented with mean and variance, as the IoU values across the three scenarios show little difference. To emphasize the impact of DM on performance and facilitate comparison, separate lines are plotted for each of the three scenarios after applying DM.