DBSCAN-based Vehicle Clustering and UAV Placement for NOMA-based Resource Management in Cellular V2X Communications

2025-04-30

AI Summary

To address spectrum efficiency optimization under fronthaul constraints in C-V2X communications, this paper proposes a two-stage resource management framework. First, dynamic vehicle clustering is performed using DBSCAN, integrating real-time position, velocity, and trajectory predictions; simultaneously, UAV 3D deployment is jointly optimized to enable efficient air-ground cooperative service. Second, within each cluster, a fronthaul-aware NOMA user grouping, power allocation, and subcarrier assignment scheme is designed, solved via a suboptimal algorithm for non-convex optimization. The key contributions are: (i) the first coupling of DBSCAN-based clustering with dynamic UAV deployment for V2X scenarios; and (ii) the formulation of a fronthaul-aware joint NOMA optimization model. Simulation results demonstrate that the proposed scheme significantly improves spectrum efficiency, enhances the minimum user rate, and reduces average service distance—outperforming state-of-the-art baseline schemes in overall performance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In the future wireless networks, terrestrial, aerial, space, and maritime wireless networks are integrated into a unified network to meet the needs of a fully connected global network. Nowadays, vehicular communication has become one of the challenging applications of wireless networks. In this article, we aim to address the radio resource management in Cellular V2X (C-V2X) networks using Unmanned Aerial Vehicles (UAV) and Non-orthogonal multiple access (NOMA). The goal of this problem is to maximize the spectral efficiency of vehicular users in Cellular Vehicle-to-Everything (C-V2X) networks under a fronthaul constraint. To solve this problem, a two-stage approach is utilized. In the first stage, vehicles in dense area are clustered based on their geographical locations, predicted location of vehicles, and speeds. Then UAVs are deployed to serve the clusters. In the second stage, NOMA groups are formed within each cluster and radio resources are allocated to vehicles based on NOMA groups. An optimization problem is formulated and a suboptimal method is used to solve it. The performance of the proposed method is evaluated through simulations where results demonstrate superiority of proposed method in spectral efficiency, min point, and distance.

Problem

• Optimize radio resource management in C-V2X networks
• Maximize spectral efficiency under fronthaul constraints
• Cluster vehicles and deploy UAVs for NOMA-based allocation

Innovation

• DBSCAN clusters vehicles by location and speed
• UAVs deployed to serve dense vehicle clusters
• NOMA groups optimize radio resource allocation