Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks

• URL: http://arxiv.org/abs/2606.16331v1
• Date: Mon, 15 Jun 2026 07:34:17 GMT
• Title: Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks
• Abstract summary: We propose a diffusion soft actor-critic (Diffusion-SAC) approach that leverages offline reinforcement learning (RL)<n>We show that Diffusion-SAC outperforms standard offline RL baselines, achieving more stable convergence and higher rewards even with limited datasets.<n>The method enhances data efficiency, reduces energy consumption, and increases throughput by more than 35 % compared to existing algorithms.
• Score: 4.225452248917631
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 from static datasets, they often struggle to generalize in low-data or dynamic conditions. To address this, we combine the robustness of CQL with the generative power of diffusion models, enabling expressive and signal-aware policy learning that generalizes beyond behavior policies. Applied to a UAV-assisted wireless network, the proposed framework minimizes transmission energy and improves fairness among devices. Simulations show that Diffusion-SAC outperforms standard offline RL baselines, achieving more stable convergence and higher rewards even with limited datasets. The method enhances data efficiency, reduces energy consumption, and increases throughput by more than 35 % compared to existing algorithms, demonstrating its potential for robust policy learning in next-generation wireless control systems.

Related papers

• Digital Twin-Assisted Adaptive Multi-Agent DRL for Intelligent Spectrum and Resource Management in Open-RAN UAV-Enabled 6G Networks [49.19977685617281]
  6G wireless networks envision 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.<n> 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.<n>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.
  arXiv  Detail & Related papers  (2026-05-31T16:19:28Z)
• xApp Empowered Resource Management for Non-Terrestrial Users in 5G O-RAN Networks [0.0]
  This paper introduces a proactive Unmanned Aerial Vehicle (UAV) mobility management xApp for Open Radio Access Network (O-RAN)<n>The proposed framework anticipates network conditions and minimises both outage probability and handover frequency through predictive optimisation.
  arXiv  Detail & Related papers  (2026-05-11T15:18:23Z)
• Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control [2.3220521366735247]
  offline RL is a promising approach for next-generation wireless networks.<n>We show that Conservative Q-Learning consistently produces more robust policies across different sources ofity.<n>These findings provide practical guidance for offline RL algorithm selection in AI-driven network control pipelines.
  arXiv  Detail & Related papers  (2026-03-04T10:41:10Z)
• Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization [79.27012080083603]
  Large language models (LLMs) offer promising tools to enhance reinforcement learning in wireless networks.<n>We propose an LLM-assisted state representation and semantic extraction to enhance the multi-agent reinforcement learning framework.
  arXiv  Detail & Related papers  (2026-01-15T01:42:39Z)
• Large Language Model-Empowered Decision Transformer for UAV-Enabled Data Collection [71.84636717632206]
  Unmanned aerial vehicles (UAVs) for reliable and energy-efficient data collection from spatially distributed devices holds great promise in supporting Internet of Things (IoT) applications.<n>We propose a joint language model (LLM) to learn effective UAV control policies.<n>LLM-CRDT outperforms benchmark online and offline methods, achieving up to 36.7% higher energy efficiency than current state-of-the-art DT approaches.
  arXiv  Detail & Related papers  (2025-09-17T13:05:08Z)
• From Pixels to CSI: Distilling Latent Dynamics For Efficient Wireless Resource Management [31.080933663717257]
  We propose a novel machine learning technique to jointly model and predict the dynamics of the control system.<n>We show that our proposed approach reduces transmit power by over 50% while maintaining control performance comparable to baseline methods.
  arXiv  Detail & Related papers  (2025-06-19T11:08:20Z)
• Offline and Distributional Reinforcement Learning for Wireless Communications [5.771885923067511]
  Traditional online reinforcement learning (RL) and deep RL methods face limitations in real-time wireless networks.<n>We focus on offline and distributional RL, two advanced RL techniques that can overcome these challenges.<n>We introduce a novel framework that combines offline and distributional RL for wireless communication applications.
  arXiv  Detail & Related papers  (2025-04-04T09:24:39Z)
• Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning [5.771885923067511]
  This work proposes a novel, resilient, few-shot meta-offline RL algorithm combining offline RL and model-agnostic meta-learning.<n>We show that the proposed few-shot meta-offline RL algorithm converges faster than baseline schemes.<n>It is the only algorithm that can achieve optimal joint AoI and transmission power using an offline dataset.
  arXiv  Detail & Related papers  (2025-02-03T11:39:12Z)
• DRL Optimization Trajectory Generation via Wireless Network Intent-Guided Diffusion Models for Optimizing Resource Allocation [58.62766376631344]
  We propose a customized wireless network intent (WNI-G) model to address different state variations of wireless communication networks. Extensive simulation achieves greater stability in spectral efficiency and variations of traditional DRL models in dynamic communication systems.
  arXiv  Detail & Related papers  (2024-10-18T14:04:38Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces [56.35676570414731]
  The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments. RISs offer a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces.
  arXiv  Detail & Related papers  (2022-05-08T06:21:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.