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

TL;DR

This paper introduces G-MSC, a novel AI-enhanced semantic communication framework for vehicular networks that improves data transmission efficiency and robustness by leveraging multi-modal data and generative AI techniques.

Contribution

The paper proposes a new GAI-enhanced semantic communication framework for V2X networks, integrating multi-modal data handling with improved robustness and efficiency.

Findings

Achieves reliable and efficient V2X communication in case studies.

Enhances semantic encoding and decoding using generative AI.

Improves robustness against noise and transmission instability.

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.