Generative Artificial intelligence-Enhanced MultiModal Semantic Communication in Internet of Vehicles: System Design and Methodologies
Jiayi Lu, Wanting Yang, Zehui Xiong, Chengwen Xing, Rahim Tafazolli, Tony Q. S. Quek, Mérouane Debbah
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. However, processing large volumes of multimodal 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 multimodal semantic communication (SemCom), referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G-MSC</i>, 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, semantic information transmission, and semantic decoding. It optimizes multimodal information fusion at the transmitter, enhances channel robustness during transmission, and mitigates noise interference at the receiver. 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 of G-MSC.