Digital Twin-Assisted Adaptive Multi-Agent DRL for Intelligent Spectrum and Resource Management in Open-RAN UAV-Enabled 6G Networks

2026-05-31

AI Summary

This work addresses the challenges of dynamic topology, nonlinear interactions, and stringent latency and energy constraints in UAV-assisted communications within 6G open radio access networks by proposing a digital twin-driven adaptive multi-agent deep reinforcement learning (MADRL) framework. The approach integrates real-time digital twin modeling with MADRL-based collaborative decision-making to jointly optimize UAV trajectories, dynamic spectrum sharing, power control, and user association, thereby enabling context-aware intelligent resource scheduling. Experimental results demonstrate that the proposed scheme significantly enhances spectral efficiency, data rates, and energy utilization, exhibiting superior performance in highly dynamic UAV–ground networks and supporting the self-evolving capabilities envisioned for 6G systems.

Abstract

The evolution toward 6G wireless networks envisions a seamlessly intelligent, Open-RAN-enabled architecture where unmanned aerial vehicles (UAVs) play a pivotal role in extending coverage, enhancing resilience, and ensuring reliable connectivity for ground users deployment. However, efficiently managing spectrum and resources in such highly dynamic UAV-assisted environments remains a major challenge due to nonlinear system interactions, mobility-induced topology variations, and stringent latency and energy constraints. To address these challenges, we propose a digital twin (DT)-assisted adaptive deep reinforcement learning (DRL) framework that enables intelligent spectrum sharing and resource allocation across distributed ground users. The complex optimization problem is decomposed into UAV trajectory optimization using particle swarm optimization (PSO) and dynamic spectrum-power-association management via multi-agent DRL (MADRL). This hybrid DT-driven approach empowers intelligent, context-aware decision-making and adaptive coordination among UAVs. Extensive simulations demonstrate significant gains in spectral efficiency, data rates, and energy utilization, showcasing a transformative path toward self-evolving, autonomous 6G UAV and ground users (GUs) connectivity.

Problem

• spectrum management
• resource allocation
• UAV-enabled 6G networks
• Open-RAN
• dynamic environments

Innovation

• Digital Twin
• Multi-Agent DRL
• Open-RAN
• UAV Trajectory Optimization
• Intelligent Spectrum Management