arXiv:2606.24483v1 Announce Type: cross Abstract: The deployment of unmanned aerial vehicles (UAV) as open radio units (O-RUs) in 6G cellular systems presents a promising opportunity to achieve scalable and adaptive network coverage. However, optimizing UAV trajectories in dynamic and unfamiliar environments remains a critical challenge, particularly due to the need for extensive retraining in each new scenario. In this paper, we introduce a novel UAV trajectory optimization framework that integrates enhanced continual transfer learning within the O-RAN architecture. The proposed system mainta
The rapid advancement of 6G cellular systems and the increasing sophistication of AI and drone technology enable practical applications for adaptive network coverage solutions.
This development allows for more resilient and scalable network infrastructure, critical for future connectivity demands in dynamic environments, with implications for military and essential services.
UAV deployment for network coverage can become more autonomous and efficient through adaptive machine learning, reducing the need for constant manual intervention and extensive retraining in new environments.
- · Telecommunications companies
- · UAV manufacturers
- · AI/ML developers
- · Defence sector
- · Traditional fixed network infrastructure providers
- · Manual network optimization services
Improved network resilience and coverage in disaster areas and remote regions.
Increased demand for advanced AI-enabled drone technology and secure communication protocols.
Potential for autonomous network deployment and self-healing systems, impacting national security and emergency response.
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