mlsanityData to 14 Oct 2026

Generative Resource Allocation for 6G O-RAN with Diffusion Policies

arXiv 2506.07880 ↗First posted 9 Jun 2025cs.NI eess.SPcited by 0

On the map: Diffusion models → Diffusion for compression and wireless

Abstract (arXiv)

Dynamic resource allocation in O-RAN is critical for managing the conflicting QoS requirements of 6G network slices. Conventional reinforcement learning agents often fail in this domain, as their unimodal policy structures cannot model the multi-modal nature of optimal allocation strategies. This paper introduces Diffusion Q-Learning (Diffusion-QL), a novel framework that represents the policy as a conditional diffusion model. Our approach generates resource allocation actions by iteratively reversing a noising process, with each step guided by the gradient of a learned Q-function. This method enables the policy to learn and sample from the complex distribution of near-optimal actions. Simulations demonstrate that the Diffusion-QL approach consistently outperforms state-of-the-art DRL baselines, offering a robust solution for the intricate resource management challenges in next-generation wireless networks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models. Builds on 9 of its 18 references that are in the map. First posted on arXiv 9 Jun 2025.

Builds on

9 of 18 references.

References · 9

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2020Denoising Diffusion Implicit Modelscited by 9,781
  4. 2022Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learningcited by 562
  5. 2023Diffusion Models for Reinforcement Learning: A Surveycited by 91
  6. 2023On the Road to 6G: Visions, Requirements, Key Technologies and Testbedscited by 77
  7. 2023Knowledge Diffusion for Distillationcited by 53
  8. 2024Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communicationscited by 18
  9. 2024Towards Secure Intelligent O-RAN Architecture: Vulnerabilities, Threats and Promising Technical Solutions using LLMscited by 3

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  1. 2025Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models
  2. 2025Generative Diffusion Models for Resource Allocation in Wireless Networks
  3. 2025Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning
  4. 2024Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN
  5. 2026Graph Signal Diffusion Models for Wireless Resource Allocation
  6. 2024Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications
  7. 2025Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning
  8. 2024Diffusion Model Based Resource Allocation Strategy in Ultra-Reliable Wireless Networked Control Systems