Single-Step Six-Dimensional Movable Antenna Reconfiguration for High-Mobility IoV: Modeling, Analysis, and Optimization

2026-05-04

AI Summary

This study addresses service outages in high-mobility vehicular networks caused by the difficulty of acquiring instantaneous channel state information (CSI) and mechanical reconfiguration delays of six-dimensional movable antennas. To overcome these challenges, the authors propose a low-complexity, single-step reconfiguration framework that operates without CSI. The approach discretizes antenna positions on a latitude–longitude grid, models reconfiguration costs using graph theory, and adaptively optimizes performance by integrating offline environmental priors with online feedback. By restricting antenna adjustments to first-order spatial neighborhoods and incorporating a periodic strategy based on predicted traffic patterns, the method achieves efficient, interruption-free reconfiguration. Simulations demonstrate that the proposed scheme significantly outperforms both fixed-antenna and global-search approaches in uplink sum rate, while incurring minimal mechanical overhead and latency, thereby offering strong feasibility and robustness.

Abstract

The Six-Dimensional Movable Antenna (6DMA) system has emerged as a promising technology to enhance wireless capacity by fully exploiting spatial degrees of freedom. However, applying 6DMA to high-mobility Internet of Vehicles (IoV) scenarios faces significant challenges, primarily due to the difficulty of acquiring instantaneous Channel State Information (CSI) and the risk of service interruptions caused by mechanical reconfiguration delays. To address these issues, this paper proposes a low-complexity, CSI-free single-step reconfiguration framework. First, we design a deterministic discrete position generation scheme based on a latitude-longitude grid with inherent topological structures. Leveraging graph theory, we explicitly model and theoretically derive the lower bounds of movement and time costs for antenna reconfiguration. Subsequently, utilizing the directional sparsity of 6DMA channels, we develop an adaptive optimization strategy that fuses offline environmental priors with online historical feedback. Furthermore, a periodic reconfiguration mechanism based on predicted cumulative vehicle distributions is introduced. By strictly restricting antenna adjustments to the first-order spatial neighborhood, the proposed single-step method effectively eliminates service interruptions. Simulation results demonstrate that the proposed scheme significantly outperforms traditional fixed and global-search-based benchmarks in terms of uplink sum rate, while incurring negligible mechanical overhead and latency, thereby validating its feasibility and robustness in highly dynamic vehicular networks.

Problem

• Six-Dimensional Movable Antenna
• Internet of Vehicles
• Channel State Information
• antenna reconfiguration
• high-mobility

Innovation

• Six-Dimensional Movable Antenna (6DMA)
• CSI-free reconfiguration
• single-step optimization
• graph-theoretic modeling
• vehicular networks