Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks
arXiv:2606.16331v1 Announce Type: new Abstract: The integration of generative artificial intelligence with wireless communication and signal processing systems has opened new avenues for intelligent, data-driven decision-making in future 6G networks. This work proposes a diffusion soft actor-critic (Diffusion-SAC) approach that leverages offline reinforcement learning (RL) enhanced by denoising diffusion probabilistic models (DDPMs) to optimize trajectory and scheduling control in unmanned aerial vehicle (UAV) networks. While offline RL methods, such as conservative Q-learning (CQL), can learn
The push for 6G and increasingly complex wireless networks necessitates more sophisticated AI-driven optimization techniques, making this research timely for future communication infrastructure development.
This work demonstrates how generative AI and offline reinforcement learning can create highly efficient and fair UAV communication networks, which is crucial for future autonomous systems and infrastructure.
The proposed Diffusion-SAC approach enhances the capacity for intelligent, distributed control in wireless networks, moving beyond traditional optimization methods to leverage advanced AI.
- · Telecommunications infrastructure providers
- · 6G network developers
- · UAV manufacturers and operators
- · AI/ML research institutions
- · Traditional wireless network optimization methods
- · Less efficient communication protocols
- · Operators reliant on manual network management
Enhanced performance and energy efficiency of UAV-assisted communication networks.
Accelerated deployment and broader adoption of intelligent, autonomous networking solutions in various sectors.
Increased reliance on complex AI models for critical infrastructure, raising new security and interpretability challenges.
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