Work

Neural-Guided Adaptive Clustering for UAV-Based User Grouping in 5G/6G Post-Disaster Networks

Year: 2025

Type: article

Abstract: In post-disaster scenarios, Unmanned Aerial Vehicles (UAVs) acting as Mobile Aerial Base Stations (MABSs) offer a flexible means of restoring communication for isolated user equipment (UE) when conventional infrastructure is unavailable. Clustering of UEs is central to UAV path planning, yet static algorithms such as Affinity Propagation Clustering (APC) often fail to generalise across diverse disaster environments and user densities. This study introduces a hybrid clustering framework that dynamically selects between APC and density-based clustering (DBSCAN), guided by a neural classifier trained on spatial UE distribution features. The chosen centroids then seed a Genetic Algorithm (GA) that evolves UAV trajectories under multiple performance indicators, including coverage, capacity, and path efficiency. Simulation results demonstrate that the hybrid clustering approach improves the adaptability and effectiveness of UAV deployments by learning context-aware clustering strategies. Compared with fixed APC-based planning, the proposed method achieves higher service ratios and more efficient UAV paths across heterogeneous disaster scenarios, validating intelligent clustering selection as a key enabler for real-time UAV-assisted communication restoration.

Source: Drones

DOI: 10.3390/drones9110731

Authors: Mohammed Sani Adam, Nor Fadzilah Abdullah, Asma Abu-Samah, Oluwatosin Ahmed Amodu, Rosdiadee Nordin

Institutions: National University of Malaysia, Universiti Putra Malaysia, Sunway University

Language: English

Topic: UAV Applications and Optimization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $3,137