Study of Cluster-Based Routing Based on Machine Learning for UAV Networks in 6G

TL;DR

This paper introduces a machine learning-based clustering framework for UAV networks in 6G, improving routing efficiency and network performance through adaptive cluster formation and head selection.

Contribution

It presents a novel ML-driven approach using XGBoost for dynamic clustering and cluster head selection tailored for 6G UAV networks, enhancing scalability and stability.

Findings

Significant reduction in delay and jitter.

Improved throughput in decentralized topologies.

Enhanced network stability and scalability.

Abstract

The sixth generation (6G) wireless networks are envisioned to deliver ultra-low latency, massive connectivity, and high data rates, enabling advanced applications such as autonomous {unmaned aerial vehicles (UAV)} swarms and aerial edge computing. However, realizing this vision in Flying Ad Hoc Networks (FANETs) requires intelligent and adaptive clustering mechanisms to ensure efficient routing and resource utilization. This paper proposes a novel machine learning-driven framework for dynamic cluster formation and cluster head selection in 6G-enabled FANETs. The system leverages mobility prediction using {Extreme Gradient Boosting (XGBoost)} and a composite optimization strategy based on signal strength and spatial proximity to identify optimal cluster heads. To evaluate the proposed method, comprehensive simulations were conducted in both centralized (5G) and decentralized (6G) topologies using realistic video traffic patterns. Results show that the proposed model achieves significant improvements in delay, jitter, and throughput in decentralized scenarios. These findings demonstrate the potential of combining machine learning with clustering techniques to enhance scalability, stability, and performance in next-generation aerial networks.