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.

Multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) is a promising solution to achieve higher performances of dual functionalities. However, the existing cell-free/bistatic MIMO ISAC networks struggle to meet strict requirements for data-intensive communication and accuracy-sensitive radar positioning. To further achieve joint enhancement, we propose a novel network where two ISAC transmitters cooperatively perform communication and target positioning, fully leveraging the advantages of cell-free/bistatic principles in communication/radar systems, referred to as bistatic-enhancement MIMO ISAC. An optimization for joint beamforming design is established to maximize the sum data rate for communication users and minimize a novel positioning-enhanced Cramér-Rao lower bound (CRB) that evaluates positioning accuracy under their corresponding requirements. The established problem is solved under two schemes: cooperative block-level and symbol-level beamforming. The solution under the former scheme is derived by an iterative behavior. Under the latter one, inter-user interference is eliminated and co-channel interference is exploited for useful signal enhancement. The problem can be converted into a convex semi-definite problem (SDP) based on semi-definite relaxation (SDR). Experimental results substantiate the effectiveness of the proposed algorithms. More importantly, the proposed bistatic-enhancement network improves positioning accuracy by 32.5% ∼ 47.5% over the conventional bistatic-site one under different schemes.

Uncrewed aerial vehicle (UAV)-enabled ISAC systems have received widespread attention due to the high mobility of UAVs with good line-of-sight (LoS) paths to ensure communication and sensing performance. However, the existing works on UAV-enabled ISAC mainly focus on optimizing communication performance (e.g., sum rate) and sensing performance, resulting in excessive energy consumption and reducing the flight endurance of the UAV. Motivated by this, we draw a trade-off between such performance and energy consumption to achieve robust and efficient UAV-enabled ISAC. In this work, we aim to maximize the worst-case energy efficiency in UAV-enabled ISAC by jointly designing the beamforming and the UAV trajectory, while ensuring the UAV energy constraints and the ISAC performance. Nevertheless, solving this problem is non-trivial due to its non-convex nature, and the high coupling of the transmit beamforming vectors and the UAV dynamics adds an additional layer of complexity. To effectively address this non-convex issue, we alternately optimize the transmit communication and sense beamforming, as well as the UAV dynamic variables to obtain a sub-optimal solution, and the algorithm complexity is lower than the existing algorithms. Experimental results show a trade-off between energy efficiency and average sum rate. Furthermore, they indicate the superiority of the proposed algorithm to enhance energy efficiency by significantly reducing energy consumption without causing excessive sum rate loss.

On-demand edge computing power networks with both stationary fog nodes co-located with cellular base stations (CFNs) and mobile fog nodes mounted on vehicles (VFNs) provide promising solutions for coping with high spatio-temporal, compute-intensive, and latency-sensitive applications. Joint scheduling and resource allocation in such a network is challenging due to the trade-off between quality of service (QoS) and energy consumption, limited onboard capacity of IoT devices and fog nodes, and urban obstructions that impede line-of-sight links. To address these issues, this work envisions a network assisted by reconfigurable intelligent surface (RIS) with a multi-layer scheme. The computation tasks are offloaded from IoT devices to VFNs and further to CFNs based on the computational demand and latency requirements, and the RIS assists with wireless communication on both links. We jointly optimized the allocation of the subcarriers, the power, the offloading task bits, the time slot, and the RIS beamforming vectors under the constraints of task input bits and computing capability, to minimize the average energy consumption. To address the non-convex issue, we first decompose it into three sub-problems, and then alternately optimize these sub-problems by adopting successive convex approximation (SCA) where a locally optimal solution can be obtained. Simulation results demonstrate the superiority of the proposed offloading strategy where RIS with a multi-layer scheme is introduced in the on-demand edge computing power networks. Also, the effectiveness, feasibility, scalability, and adaptability of the designed algorithm are verified.

Unmanned aerial vehicle (UAV)-enabled massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) is an emerging platform to perform communication and sensing efficiently and flexibly. However, the existing works barely consider the radar probing tasks and neglect the benefits of the dedicated sensing signal. In this paper, we focus on joint optimizations in radar probing tasks, and a novel indicator is introduced, namely radar probing error. Two optimizations in radar probing tasks are established: i) joint transmit beamforming design for large-scale regional radar probing and communication task; ii) joint transmit beamforming and UAV trajectory design for communication enhancement and radar probing task. For the former task, we adopt both communication and novel sensing precoders to further support the MIMO radar. A semidefinite relaxation is utilized to relax the original non-convex problem, which is proven to be tight. For the latter task, we adopt block coordinate descent to alternately optimize the precoders and UAV trajectory where the fractional programming approach and successive convex approximation are further adopted. Experiment results testify the validation of the proposed methods for radar probing tasks in UAV-enabled MIMO ISAC. Moreover, results show the fundamental trade-off between the dual functions and reveal the effectiveness of the introduced sensing precoder.

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.

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.

The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. Edge/Fog computing moves computation close to the edge to shorten network latency. vehicular fog computing (VFC) proposes to complement stationary fog nodes co-located with cellular base stations with mobile ones carried by moving vehicles. Previous works on VFC mainly focus on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. The complexity of this problem results from the spatio-temporal dynamics of vehicular traffic, varying computing resource demand generated by vehicular applications, the trade-off between the quality of service and cost expenditure, and the mobility of client vehicles and vehicular fog nodes. To address these challenges, we present our methodology, framework, and studies on capacity planning for VFC.

The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities [a.k.a fog nodes (FNs)]. In other words, VFC proposes to complement stationary FNs co-located with cellular base stations with mobile ones carried by moving vehicles (e.g., buses). Previous works on VFC mainly focus on optimizing the assignments of computing tasks among available FNs. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. The complexity of this problem results from the spatiotemporal dynamics of vehicular traffic, varying computing resource demand generated by vehicular applications, and the mobility of FNs. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile FNs to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatiotemporal variation in both demand and supply. Using real-world traffic data and application profiles, we analyze the cost efficiency potential of VFC in the long term. We also evaluate the impacts of traffic patterns on the capacity plans and the potential cost savings. We find that high traffic density and significant hourly variation would lead to dense deployment of mobile FNs and create more savings in operational costs in the long term.