As an essential component, roadside units (RSUs) play an indispensable role in realizing Vehicle-to-Everything (V2X) by seamlessly connecting various intelligent devices and vehicles. To facilitate the construction of V2X, much research has been done in designing effective RSU deployment strategies. However, most of these efforts are largely limited by design utility and deployment scalability. To address the limitations of previous works, this paper proposes a general RSU deployment framework, Greta, which can evaluate candidate deployment sites from different perspectives with rich input data, and satisfy different requirements on optimization metrics. To this end, we model the general RSU deployment problem as a customized reinforcement learning (RL) problem that intelligently explores the deployment environment to find a good deployment strategy. Specifically, we design an effective data profiling network to extract features from multi-modality input data. These extracted features are gradually weighted, fused, and encoded as part of the state representation of the RL model. We further design new reward functions considering various deployment metrics and propose an action space pruning scheme to speed up model training. We implement a prototype system of Greta and extensively evaluate its performance using real-world data. The results show Greta achieves remarkable performance gains compared to recent RSU deployment methods.

The evolution towards 6G is set to transform Vehicle-to-Everything (V2X) networks by introducing advanced technologies such as Six-Dimensional Movable Antenna (6DMA). This technology endows Roadside Units (RSUs) with dynamic beam-steering capabilities, enabling adaptive coverage. However, traditional RSU deployment strategies, optimized for static coverage, are fundamentally mismatched with these new dynamic capabilities, leading to a critical deployment–optimization mismatch. This paper addresses this challenge by proposing DyDO, a novel Dynamic Deployment and Optimization framework for the utilization of 6DMA-RSUs. Our framework strategically decouples the problem into two modules operating on distinct timescales. On a slow timescale, an offline deployment module analyzes long-term historical traffic data to identify optimal RSU locations. This is guided by a newly proposed metric, the Dynamic Potential Score (DPS), which quantifies a location’s intrinsic value for dynamic adaptation by integrating spatial concentration, temporal volatility, and traffic magnitude. On a fast timescale, an online control module employs an efficient Sequential Angular Search (SAS) algorithm to perform real-time, adaptive beam steering based on immediate traffic patterns. Extensive experiments on a large-scale, real-world trajectory dataset demonstrate that DyDO outperforms conventional static deployment methodologies. This work highlights the necessity of dynamic-aware deployment to fully unlock the potential of 6DMA in future V2X systems.