Publisher Copyright: © 2026 IEEE.

Vehicular Edge Computing (VEC) is an essential part of the Internet of Vehicles (IoV) due to its low latency by moving the computational resources close to the edge. Although the introduction of network slicing into VEC improves resource utilization through dynamic resource allocation based on real-time demands and priorities, it increases the deployment and operational costs. In view of this, this paper envisions a resource allocation strategy for VEC based on network slicing technique, in which the tasks involved are not only dynamic but also heterogeneous. To minimize the system cost (including resource consumption and computation, network slice maintenance and reconfiguration costs), this paper proposes CST-RL, a confidence-based self-adjusting two-timescale reinforcement learning algorithm. This solution performs resource allocation and activation scheduling for network slices on a large timescale, while allocating slices to heterogeneous tasks on a short timescale to meet dynamic demands. In addition, we innovatively utilize critic in reinforcement learning to predict and compare the expected benefits of network slices with versus without reconfiguration. We introduce the Random Network Distillation (RND) technique to assess the confidence level of these benefits, thus providing guidance for network slices to automatically decide whether and when to undergo reconfiguration. Finally, we demonstrate the effectiveness and superiority of CST-RL through simulations. Results show that CST-RL yields 27.77% lower system cost compared to the scheme without network slicing and 15.15% lower system cost compared to performing constant network slicing configuration, with guaranteed Quality-of-Service.

The integration of intelligent reflective surfaces (IRS) on unmanned aerial vehicles (UAVs), termed UAV-IRS, to bolster wireless communications has emerged as a hotspot of academic research and industrial application. In this paper, we investigate the problem of secure communication in the harsh communication environment assisted by multiple UAV-IRSs, where the UAV-IRSs act as relays to assist the downlink secure communication between the base station and the users. To maximize the security sum rate between the base station and the users, the trajectory planning and phase shift design of multiple UAV-IRS needs to be jointly optimized. To solve this complex non-convex optimization problem, we introduce a distributed collaborative optimization scheme for multiple UAV-IRSs called credit-aware cooperative multi-agent reinforcement learning (MARL), which takes MARL as the base algorithm, and then solves the credit allocation problem among multiple UAV-IRSs by using cooperative game theory to facilitate exploration, and finally constrains non-cooperative behaviors among UAV-IRSs by using the primal-dual optimization algorithm to promote cooperation. Finally, the effectiveness and superiority of the proposed scheme is verified by comprehensive simulation experiments.

Uncrewed aerial vehicle (UAV)-assisted integrated sensing, computation, and communication (ISCC) network enables the entire data analysis process for practical applications. The existing works of UAV-assisted ISCC merely consider a single data source, and there still exist gaps in the collection of environmental data via multiple sources. Motivated by this, we envision a novel UAV-assisted air-ground collaborative ISCC network that fully explores the cooperation between aerial UAVs and ubiquitous ground Internet of Things (IoT) devices. To achieve effective, efficient, and fair joint task scheduling and resource allocation, an optimization is established to minimize two novel indicators, i.e., computation offloading and sensing penalty indices, subject to constraints of boundary, anti-collision, and UAV energy consumption. To tackle this problem, a deep reinforcement learning (DRL) framework is proposed where three advanced DRL algorithms are included under centralized and decentralized control schemes. In former scheme, the central controller makes globally optimal decisions. In latter scheme, multiple agents decide independently based on local information. We demonstrate a forest fire monitoring use case simulated in a national forest park. Results show the mutually interfering, competitive, and beneficial relationships among triple functionalities. Besides, our solution outperforms three state-of-the-art baselines in terms of effectiveness and efficiency.

Intelligent Transportation Systems (ITS) urgently require real-time data collection with minimized Age of Information (AoI), yet face critical challenges from high-dynamic traffic environments and unstable wireless channels. By virtue of the low deployment cost and the high-speed mobility, Uncrewed Aerial Vehicle (UAV) bring us a feasible approach to the aforementioned problem. Nevertheless, such a problem is far from trivial due to lot of factors ranging from the highly dynamic communication environment, the dimension-varying input/output for each UAV, to the extremely large solution space for all the UAVs as a whole in a distributed collaborative manner. Although existing Multi-Agent Reinforcement Learning (MARL) solutions are widely used to address the above challenges, they all rely on fixed-dimensional input/output processing (e.g., padding/truncation strategies), leading to redundancy or loss of information due to dimensionality-changing scenarios. To address this gap, we proposed an improvement scheme based on attention-driven MARL, which redesigns the policy and critic network based on the attention mechanism to help UAVs extract critical information from dimension-varying data in an accurate and efficient manner. Finally, we verify the superiority and robustness of the proposed scheme through multiple sets of experiments with multiple different aspects. The simulation results show that the proposed scheme is scalable and efficient, and the weighted average AoI under different scenarios is lower than the existing state-of-the-art schemes by$13.1\%\sim 56.2\%$.

Joint task scheduling and resource allocation for unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) in emergency rescue activities has become an essential and challenging problem. However, the existing works have only considered such a problem for standalone UAV networks without considering the cooperation between UAVs and ground base stations (BSs), nor have they considered the uncertainty in terms of the availability of BSs due to damage/reconstruction in disaster events. In this paper, we consider a novel post-disaster UAV-assisted ISAC system where the UAVs are used to supplement the networking capacity of out-of-service ground BSs while using their radio signals for sensing. We apply transfer learning with deep reinforcement learning (DRL) to learn task scheduling and resource allocation strategies that can rapidly adapt to uncertainty in the environment. Experimental results show that the proposed algorithm outperforms the state-of-the-art in both communication and sensing performance and convergence speed. Moreover, the transfer learning-based DRL shows faster convergence and better robustness when the availability of BSs suddenly changes.

Mobile edge computing (MEC) has attracted widespread attention due to its ability to effectively alleviate the cloud computing load and significantly reduce latency. However, the potential eavesdroppers challenge the security of the MEC systems and the rapid development of artificial intelligence (AI) has made this security situation more severe. In most existing studies, the eavesdroppers are non-intelligent and it is assumed that they are fixed or move in a simple manner. Obviously, there is a gap from such an assumption to the real conditions that the eavesdropping unmanned aerial vehicles (UAVs) may adjust their flight paths intelligently. To better reflect real-world scenarios, we consider a multi-UAV-assisted MEC system in the presence of intelligent eavesdroppers and propose an adversarial multi-agent reinforcement learning (MARL)-based scheme for secure computational offloading and resource allocation. With this scheme, we aim to solve the zero-sum game between the legitimate UAVs and the eavesdropping UAVs, in which the two types of UAVs take turns acting as the agents of MARL to alternately optimize their respective opposing objectives. The simulation experimental results indicate that the proposed scheme significantly outperforms the existing baseline methods in dealing with the intelligent eavesdropping UAVs, and ensures high energy efficiency of Internet of Things (IoT) devices even in the worst-case scenario when dealing with potential eavesdropping threats.

Mobile-edge computing (MEC) as a promising technology enables it to satisfy ever-increasing demands for low-latency and ultrareliable services. However, due to the limitations of computing capability and the dynamic network environment, it is challenging to process massive data with low latency. In this article, we consider a dynamic MEC network with a high-performance edge server, multiple time-varying channels, and multiple mobile devices. We aim to find a policy that can maximize the processing success rate of computational tasks and the fairness index of the system while minimizing the process delays. To this end, we propose a heuristic-assisted multiagent reinforcement learning (RL)-based framework to realize the joint optimization of computation offloading and resource allocation. On the one hand, heuristic search is introduced in this framework to find a better resource allocation policy in edge servers and further assist the multiagent RL algorithm to determine offloading policy in mobile devices. On the other hand, a novel parameterized multiagent RL algorithm based on soft actor–critic (SAC) is also proposed to broaden the effectiveness and availability of the proposed framework. Simulation results of the average cumulative reward, success rate, processing delay, and fairness index fully verify the superiority of the proposed framework and algorithm for supporting this problem.

In this paper, multi-UAVs serve as mobile aerial ISAC platforms to sense and communicate with on-ground target users. To optimize the communication and sensing performance, we formulate a joint user association, UAV trajectory planning and power allocation problem to maximize the minimum weighted spectral efficiency among UAVs. This paper exploits the centralized and the decentralized deep reinforcement learning (DRL) solutions to solve the sequential decision-making problem. On one hand, we first introduce the centralized soft actor-critic (SAC) algorithm. Then, we explore the equivalent transformation of the optimization objective based on symmetric group, propose the random and the adaptive data augmentation schemes to design the replay memory buffer of SAC, and accordingly propose SAC algorithms assisted by data augmentation to tackle the transformed problem. On the other hand, the multi-agent soft actor-critic (MASAC), a decentralized solution, is also introduced to solve this sequential decision-making problem. The experiment results reveal the effectiveness of the centralized and the decentralized solutions in considered scenarios. Specifically, the SAC assisted by the adaptive scheme significantly outperforms other centralized solutions in the training speed and the weighted spectral efficiency. Meanwhile, the decentralized MASAC algorithm behaves best in the early training speed.