Year: 2025
Type: preprint
Abstract: Accurate localization is critical for vehicle-to-infrastructure (V2I) communication systems, especially in urban areas where GPS signals are often obstructed by tall buildings, leading to significant positioning errors, necessitating alternative or complementary techniques for reliable and precise positioning in applications like autonomous driving and smart city infrastructure. This paper proposes a multimodal contrastive learning regression based localization framework for V2I scenarios that combines channel state information (CSI) with visual information to achieve improved accuracy and reliability. The approach leverages the complementary strengths of wireless and visual data to overcome the limitations of traditional localization methods, offering a robust solution for V2I applications. Simulation results demonstrate that the proposed CSI and vision fusion model significantly outperforms traditional methods and single modal models, achieving superior localization accuracy and precision in complex urban environments.
Source: arXiv (Cornell University)
Authors: Zheng, Can, He, Jiguang, Kang, Chung G., Cai, Guofa, Wymeersch, Henk
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7077974976
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