Author(s)

Zhuo Chen ¹, Yunjiang Liu ¹

Affiliation(s)

¹ School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, China

Corresponding Author

Yunjiang Liu

ABSTRACT

The 6G vehicle-infrastructure cooperative integrated sensing and communication (ISAC) system constitutes a key technological enabler for high-level autonomous driving. In this paper, a four-dimensional collaborative architecture integrating vehicles, roadside infrastructure, cloud, and satellites is designed. A LiDAR-assisted geometry-based stochastic channel model is established, and a joint optimization objective for localization and beam tracking is formulated. To address the scarcity of labeled data, a self-supervised localization algorithm based on the LeJEPA framework is proposed. Simulation results demonstrate that the localization error can be controlled within 10 cm. In addition, a robust beam tracking algorithm is developed based on target-state estimation and S-procedure-based convex optimization, and a bidirectional collaborative optimization framework is constructed. The results indicate that the proposed system and algorithms can effectively improve localization accuracy and beam tracking robustness while reducing the probability of communication interruption, thereby satisfying the requirements of 6G vehicle-infrastructure cooperative scenarios.

KEYWORDS

6G; vehicle-infrastructure cooperation; integrated sensing and communication; self-supervised learning; localization algorithm

CITE THIS PAPER

Zhuo Chen, Yunjiang Liu. 6G Vehicle-Infrastructure Cooperative Integrated Sensing and Communication: A Self-Supervised Learning-Based Localization and Robust Beam Tracking Algorithm. Journal of Electronics and Information Science (2026). Vol. 11, No. 1, 26-33. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2026.110104.

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