Integrated sensing and communication (ISAC) has been recognized as a key technology in sixth-generation wireless networks, and the additional spatial degrees of freedom obtained by movable antenna (MA) technology can significantly improve the performance of ISAC systems. This paper considers an ISAC-assisted vehicle-to-infrastructure (V2I) network, where extended kalman filter-based prediction is combined with real-time optimization to jointly optimize transmit antenna positions and beamforming and power allocation vectors in dynamic environments. We propose two algorithms: a preprocessing-schur complement-projected gradient ascent algorithm for scenarios without sensing quality of service (QoS) constraints, which explores the potential range of sensing performance to provide reference and warm-starting for subsequent constrained optimization; and a heuristic reflective projected dynamic particle swarm optimization algorithm for sensing QoS-constrained scenarios, which achieves substantial performance gains under non-convex constraints with a small number of iterations. Simulation results demonstrate that these approaches enhance both the communication sum-rate and the lower of the Cram\'er-Rao lower bound of motion parameter estimation, validating the effectiveness of MA-assisted beamforming in dynamic V2I ISAC networks.
@article{arxiv.2512.04802,
title = {Movable Antenna Assisted Flexible Beamforming for Integrated Sensing and Communication in Vehicular Networks},
author = {Luyang Sun and Zhiqing Wei and Haotian Liu and Kan Yu and Zhendong Li and Zhiyong Feng},
journal= {arXiv preprint arXiv:2512.04802},
year = {2025}
}