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.
AERIS: Offline Policy Improvement for Multi-UAV Integrated Sensing and Communication
Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；The University of Hong Kong（香港大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Toronto Metropolitan University（多伦多都会大学）
Department of Computer and Information Science, Northumbria University（诺森比亚大学计算机与信息科学系）；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦玛丽女王大学电子工程与计算机科学学院）；National Mobile Communications Research Lab, Southeast University（东南大学国家移动通信研究实验室）；Purple Mountain Laboratories（紫金山实验室）；School of Electronics and Computer Science, University of Southampton（南安普顿大学电子与计算机科学学院）
An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV' UE-load and the overall energy consumption of UEs. The above optimization problem includes both integer and continues variables and it is challenging to solve. To address the above problem, a multi-agent deep reinforcement learning based trajectory control algorithm is proposed for ma…
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks. In this paper, we aim to find collision-free paths for multiple cellular-connected UAVs, while satisfying requirements of connectivity with ground base stations (GBSs) in the presence of a dynamic jammer. We first formulate the problem as a sequential decision making problem in discrete domain, with connectivity, collision avoidance, and kinematic constraints. We, then, propose an offline temporal difference (TD) learning algorithm with online signal-to-interference-plus-noise ratio (SINR) mapping to solv…
PyCFRL: A Python library for counterfactually fair offline reinforcement learning via sequential data preprocessing
Department of Statistics, University of Michigan, USA（统计学系，密歇根大学）；Department of Biostatistics, University of Michigan, USA（生物统计学系，密歇根大学）；Department of Statistics, London School of Economics, UK（统计学系，伦敦经济学院）；Department of Health Behavior and Health Equity, School of Public Health, University of Michigan, USA（健康行为与健康公平系，公共卫生学院，密歇根大学）
Large Language Model-Empowered Decision Transformer for UAV-Enabled Data Collection
School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦女王学院电子工程与计算机科学学院）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究中心）；Department of Electronics and Information Convergence Engineering, Kyung Hee University（韩国庆熙大学电子与信息融合工程系）
Hybrid Adaptive Conformal Offline Reinforcement Learning for Fair Population Health Management
Waymark, San Francisco, CA, USA（Waymark）；San Francisco General Hospital, University of California San Francisco, San Francisco, CA, USA（旧金山通用医院，加州大学旧金山分校）；University of California San Francisco, San Francisco, CA, USA（加州大学旧金山分校）；University of Pennsylvania, Philadelphia, PA, USA（宾夕法尼亚大学）
Aerial robots are increasingly being utilized for environmental monitoring and exploration. However, a key challenge is efficiently planning paths to maximize the information value of acquired data as an initially unknown environment is explored. To address this, we propose a new approach for informative path planning based on deep reinforcement learning (RL). Combining recent advances in RL and robotic applications, our method combines tree search with an offline-learned neural network predicting informative sensing actions. We introduce several components making our approach applicable for r…
National University of Defense Technology（国防科技大学）；Tsinghua University（清华大学）
The unmanned aerial vehicle (UAV) plays an vital role in various applications such as delivery, military mission, disaster rescue, communication, etc., due to its flexibility and versatility. This paper proposes a deep reinforcement learning method to solve the UAV online routing problem with wireless power transfer, which can charge the UAV remotely without wires, thus extending the capability of the battery-limited UAV. Our study considers the power consumption of the UAV and the wireless charging process. Unlike the previous works, we solve the problem by a designed deep neural network. The…
Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training
Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg.（自动化与机器人研究组，安全、可靠性与信任 interdisciplinary centre（SnT），卢森堡大学）
In unmanned aerial vehicle (UAV) applications, the UAV's limited energy supply and storage have triggered the development of intelligent energy-conserving scheduling solutions. In this paper, we investigate energy minimization for UAV-aided communication networks by jointly optimizing data-transmission scheduling and UAV hovering time. The formulated problem is combinatorial and non-convex with bilinear constraints. To tackle the problem, firstly, we provide an optimal relax-and-approximate solution and develop a near-optimal algorithm. Both the proposed solutions are served as offline perform…
The American College of Greece（美国希腊学院）；Aalborg University（奥尔堡大学）；Nokia Bell-Labs France（诺基亚贝尔实验室法国分部）
In this work, we optimize the 3D trajectory of an unmanned aerial vehicle (UAV)-based portable access point (PAP) that provides wireless services to a set of ground nodes (GNs). Moreover, as per the Peukert effect, we consider pragmatic non-linear battery discharge for the battery of the UAV. Thus, we formulate the problem in a novel manner that represents the maximization of a fairness-based energy efficiency metric and is named fair energy efficiency (FEE). The FEE metric defines a system that lays importance on both the per-user service fairness and the energy efficiency of the PAP. The for…
Michigan Technological University（密歇根理工大学）；University of Southern California（南加州大学）；Columbia University（哥伦比亚大学）；Dalhousie University（达尔豪斯大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；University of Waikato（怀卡托大学）
Bias in machine learning has rightly received significant attention over the last decade. However, most fair machine learning (fair-ML) work to address bias in decision-making systems has focused solely on the offline setting. Despite the wide prevalence of online systems in the real world, work on identifying and correcting bias in the online setting is severely lacking. The unique challenges of the online environment make addressing bias more difficult than in the offline setting. First, Streaming Machine Learning (SML) algorithms must deal with the constantly evolving real-time data stream.…
