In recent years, advances in quantum computing have been driven by substantial improvements in both the number and quality of qubits. As the field progresses, there is growing interest in interconnecting quantum systems to enable scalable computation through Distributed Quantum Computing (DQC) architectures. Consider a distributed quantum application executed across multiple Quantum Processing Units (QPUs) within a quantum data center, where remote gates require establishing entanglement between different QPUs. The creation of such end-to-end entanglement can lead to network congestion and resource contention. To address these challenges, we propose a resource management framework that maximizes fidelity-guaranteed throughput while satisfying dependency constraints. We first formulate the problem as a Mixed-Integer Linear Programming (MILP) model to provide a performance benchmark. Building on this, we develop efficient approximate scheduling algorithms that achieve performance comparable to the optimization solver. Although a trade-off exists between execution time and network throughput, simulation results demonstrate that one of the proposed strategies, Weighted Group Least Resource First (WGLRF), closely approximates the solver’s performance across most scenarios. These findings suggest that the lightweight strategy is sufficient for current DQC settings, offering a practical solution for managing remote-gate resource contention in distributed quantum circuits and improving overall system performance.

Scalable quantum networks must support concurrent entanglement requests from multiple users, yet existing routing protocols fail when users compete for shared repeater resources and waste fragile quantum states that decay rapidly and cannot be buffered like classical data. This paper presents RADAR-Q, a resource-aware decentralized routing protocol that embeds real-time resource contention directly into path selection. Unlike prior designs that either require global coordination or route all traffic through a central anchor, RADAR-Q makes intelligent local decisions by balancing three factors: (1) path length and link fidelity, (2) instantaneous availability of quantum memory at each node, and (3) the number of intermediate Bell-State Measurement (BSM) operations needed to connect a source–destination pair. By identifying the Nearest Common Ancestor (NCA) within a DODAG routing hierarchy, RADAR-Q localizes entanglement swapping close to the communicating users—avoiding unnecessary detours through the network center and reducing both the BSM chain length and qubit exposure to decoherence. We evaluate RADAR-Q on grid and random topologies—representing regular and irregular network fabrics, respectively—against state-of-the-art synchronous and root-centric asynchronous baselines. Results demonstrate that RADAR-Q achieves 2.5× and 7.6× higher aggregate throughput than synchronized and root-centric asynchronous designs, respectively. While baseline protocols suffer catastrophic fidelity collapse below the 0.5 distillation threshold [2] under high load, RADAR-Q consistently maintains end-to-end fidelity above 0.76—ensuring every generated pair remains physically usable for downstream quantum applications. Furthermore, RADAR-Q exhibits near-perfect fairness (Jain’s Fairness Index 96–98

Mobile edge computing (MEC)-enabled air-ground networks are a key component of 6G, employing aerial base stations (ABSs) such as unmanned aerial vehicles (UAVs) and high-altitude platform stations (HAPS) to provide dynamic services to ground IoT devices (IoTDs). These IoTDs support real-time applications (e.g., multimedia and Metaverse services) that demand high computational resources and strict quality of service (QoS) guarantees in terms of latency and task queue management. Given their limited energy and processing capabilities, IoTDs rely on UAVs and HAPS to offload tasks for distributed processing, forming a multi-tier MEC system. This paper tackles the overall energy minimization problem in MEC-enabled air-ground integrated networks (MAGIN) by jointly optimizing UAV trajectories, computing resource allocation, and queue-aware task offloading decisions. The optimization is challenging due to the nonconvex, nonlinear nature of this hierarchical system, which renders traditional methods ineffective. We reformulate the problem as a multi-agent Markov decision process (MDP) with continuous action spaces and heterogeneous agents, and propose a novel variant of multi-agent proximal policy optimization with a Beta distribution (MAPPO-BD) to solve it. Extensive simulations show that MAPPO-BD outperforms baseline schemes, achieving superior energy savings and efficient resource management in MAGIN while meeting queue delay and edge computing constraints.

Quantum networks depend critically on the efficient management of inherently unstable quantum resources, primarily entanglement. Traditional resource allocation methods often fail to account for the complex, dynamic trade-offs between entanglement distribution distance, fidelity, and decoherence, leading to significant wastage and underutilization. This paper studies the quantum resource allocation problem as a machinelearning optimization problem. We compare two distinct dynamic solvers: a Genetic Algorithm-based solver (Q-GA) and a Deep Reinforcement Learning-based solver (Q-DRL) in their ability to optimize resource allocation and address the resource allocation optimization framework. The Q-GA evolves a population of allocation strategies over time, whereas the Q-DRL agent learns a policy to make real-time distribution decisions. Our comparative analysis focuses on a multi-objective function designed to maximize the successful delivery of entangled pairs and their fidelity, while minimizing resource wastage due to decoherence, operational noise, the probabilistic nature of entanglement generation, and swapping operations. The study shows that in a variety of quantum resource allocation environments, both ML-based approaches significantly outperform conventional static heuristics. Notably, the Q-DRL solver exhibits superior adaptability to network dynamics, achieving a higher success rate and reduced resource wastage than the Q-GA. These findings establish their efficacy as a robust solution for the next-generation quantum resource allocation management.

The Metaverse faces complex resource allocation challenges due to diverse Virtual Environments (VEs), Digital Twins (DTs), dynamic user demands, and strict immersion needs. This paper introduces CIVIC (Cooperative Immersion Via Intelligent Credit-sharing), a novel framework optimizing resource sharing among multiple Metaverse Service Providers (MSPs) to enhance user immersion. Unlike existing methods, CIVIC integrates VE rendering, DT synchronization, credit sharing, and immersion-aware provisioning within a cooperative multi-MSP model. The resource allocation problem is formulated as two NP-hard challenges: a non-cooperative setting where MSPs operate independently and a cooperative setting utilizing a General Credit Pool (GCP) for dynamic resource sharing. Using Deep Reinforcement Learning (DRL) for tuning resources and managing cooperating MSPs, CIVIC achieves 12-36

To address the limitations of existing wireless networks for demanding applications like brain-computer interfaces and intelligent transportation systems, we propose an advanced framework for joint resource allocation and task offloading across integrated terrestrial and non-terrestrial networks (TN-NTN). This framework utilizes multiple layers, including ground users, UAVs, HAPs, and satellites, to improve service quality and immersive experiences, particularly in scenarios like Metaverse applications. Ground users request resources, while UAVs and HAPs serve as resource providers, and satellites ensure reliable communication during emergencies. A double auction-based incentive scheme is employed in which operators control UAV and HAP resources to maximize utility, and users aim to minimize computation costs and protect data privacy. To handle the complexity of the operator-user interaction, which results in an NP-hard optimization problem, we applied a hierarchical multi-agent federated deep reinforcement learning (FeDRL) approach. Our simulation results demonstrate that the FeDRL algorithm significantly improves social welfare by 6.38%, 17.43%, and 28.73% over modified MADDPG, FRL, and DDPG algorithms, respectively.

Intent-based networks (IBNs) are gaining prominence as an innovative technology that automates network operations through high-level request statements, defining what the network should achieve. In this work, we introduce IntAgent, an intelligent intent LLM agent that integrates NWDAF analytics and tools to fulfill the network operator's intents. Unlike previous approaches, we develop an intent tools engine directly within the NWDAF analytics engine, allowing our agent to utilize live network analytics to inform its reasoning and tool selection. We offer an enriched, 3GPP-compliant data source that enhances the dynamic, context-aware fulfillment of network operator goals, along with an MCP tools server for scheduling, monitoring, and analytics tools. We demonstrate the efficacy of our framework through two practical use cases: ML-based traffic prediction and scheduled policy enforcement, which validate IntAgent's ability to autonomously fulfill complex network intents.

In quantum networks, one way to communicate is to distribute entanglements through swapping at intermediate nodes. Most existing work primarily aims to create efficient two-party end-to-end entanglement over long distances. However, some scenarios also require remote multipartite entanglement for applications such as quantum secret sharing and multi-party computation. Our previous study improved end-to-end entanglement rates using an asynchronous, tree-based routing scheme that relies solely on local knowledge of entanglement links, conserving unused entanglement and avoiding synchronous operations. This article extends this approach to multipartite entanglements, particularly the three-party Greenberger-Horne-Zeilinger (GHZ) states. It shows that our asynchronous protocol outperforms traditional synchronous methods in entanglement rates, especially as coherence times increase. This approach can also be extended to four-party and larger multipartite GHZ states, highlighting the effectiveness and adaptability of asynchronous routing for multipartite scenarios across various network topologies.

In the Metaverse, real-time digital twin (DT) updates enable immersive, interactive environments by reflecting real world states. Metaverse can engage its users to share data in order to ensure the completeness of the DT. However, the limitations and heterogeneity in IoT devices' computation and transmission resources are critical challenges to synchronizing the vast volume of real-world objects with their digital replicas. In this work, we propose a novel adaptive Vision Transformer (ViT)- based semantic communication (SemCom) system to extract semantic information from raw data collected by IoT devices. The system dynamically adjusts the model size and computational complexity according to the resource constraints of individual IoT devices, enables broader participation of heterogeneous devices, enhances feature extraction capabilities, and ensures completeness and efficient DT representation. We formulate our problem as an utility-maximization optimization to manage ViT complexity under resource constraints, allowing the Metaverse Services Provider (MSP) to select high-performing IoT devices. To guide the optimization, we profile ViT models with varying architectural complexities and conduct an empirical analysis to capture the relationship between model scale and performance. To address the scalability and privacy limitations of the optimization, we propose a decentralized solution where each IoT device independently optimizes its own utility under local constraints. The MSP, in turn, selects among these devices to ensure quality and maximize its overall utility. We show that our distributed solution achieves near-optimal performance and significantly outperforms other approaches in terms of MSP utility, semantic data quality, and overall IoT utility.

Although multi-tier vehicular Metaverse promises to transform vehicles into essential nodes -- within an interconnected digital ecosystem -- using efficient resource allocation and seamless vehicular twin (VT) migration, this can hardly be achieved by the existing techniques operating in a highly dynamic vehicular environment, since they can hardly balance multi-objective optimization problems such as latency reduction, resource utilization, and user experience (UX). To address these challenges, we introduce a novel multi-tier resource allocation and VT migration framework that integrates Graph Convolutional Networks (GCNs), a hierarchical Stackelberg game-based incentive mechanism, and Multi-Agent Deep Reinforcement Learning (MADRL). The GCN-based model captures both spatial and temporal dependencies within the vehicular network; the Stackelberg game-based incentive mechanism fosters cooperation between vehicles and infrastructure; and the MADRL algorithm jointly optimizes resource allocation and VT migration in real time. By modeling this dynamic and multi-tier vehicular Metaverse as a Markov Decision Process (MDP), we develop a MADRL-based algorithm dubbed the Multi-Objective Multi-Agent Deep Deterministic Policy Gradient (MO-MADDPG), which can effectively balances the various conflicting objectives. Extensive simulations validate the effectiveness of this algorithm that is demonstrated to enhance scalability, reliability, and efficiency while considerably improving latency, resource utilization, migration cost, and overall UX by 12.8%, 9.7%, 14.2%, and 16.1%, respectively.

Sixth-generation (6G) wireless networks require massive connectivity, high spectral efficiency (SE), and energy efficiency (EE). Although conventional non-orthogonal multiple access (NOMA) improves spectrum utilization by allowing multiple users to share the same subchannel through power-domain multiplexing, applying NOMA to large user groups significantly increases successive interference cancellation (SIC) complexity and intra-group interference. To address this limitation, clustered NOMA (C-NOMA) groups users into small subclusters where SIC is performed over fewer users, thereby improving scalability while reducing decoding complexity. Combining C-NOMA with hybrid beamforming (HB) further enhances SE and lowers power consumption by serving each subcluster through a dedicated analog beam with fewer radio-frequency chains. In this paper, we develop a cooperative resource-allocation framework for C-NOMA-enabled multi-unmanned aerial vehicle (UAV) millimeter-wave (mmWave) networks with two-dimensional (2D) HB. UAVs equipped with 2D uniform planar array antennas serve as aerial base stations to enhance line-of-sight (LoS) connectivity. We formulate an EE-maximization problem that jointly considers user subclustering, power allocation (PA), and subchannel assignment (SA) under transmit-power and SIC constraints. User subclustering is first performed through head selection and channel-disparity-based pairing to enable efficient C-NOMA transmission. Given the resulting structure, the 2D HB and joint PA/SA optimization problem is solved via a two-stage approach: a heuristic 2D HB construction followed by a multi-agent deep deterministic policy gradient (MADDPG) algorithm for cooperative PA and SA optimization under centralized training and decentralized execution (CTDE). Simulation results demonstrate that the proposed framework consistently improves EE over representative benchmarks across different network configurations.

Semantic communication (SemCom) leverages artificial intelligence (AI) to prioritize meaningful information exchange, significantly enhancing efficiency and resource utilization in next-generation wireless networks. Despite its potential, practical deployment within multi-tier, heterogeneous edge-cloud systems presents substantial challenges, including diverse device computational capabilities, varying communication resources, and semantic mismatches between user encoders and edge-server decoders. To overcome these challenges, we propose SemCom-OPTIMA, a comprehensive optimization framework specifically designed for maximizing semantic image reconstruction accuracy in multi-user, multi-edge environments under strict latency and energy constraints. The problem is formulated as a joint mixed-integer nonlinear programming (MINLP) model, incorporating user-edge associations, semantic masking ratios, CPU frequency scaling, and joint power-bandwidth allocation. Given its NP-hard complexity, we present a multi-stage decomposition algorithm, solving iteratively tractable subproblems for semantic-computational allocation, channel resource management, and adaptive user-edge association, while ensuring convergence and feasibility. Distinctively, this work is grounded in extensive empirical analysis involving a rigorous 600,000-sample design-space exploration spanning masking ratios (0-0.99), signal-to-noise ratios (1-16 dB), and diverse semantic image classes (1000 ImageNet labels). Experiments conducted on a realistic 10-user, 3-edge testbed demonstrate that SemCom-OPTIMA consistently achieves a 10-12% higher peak signal-to-noise ratio (PSNR) and robustly meets energy and latency constraints compared to state-of-the-art baseline methods.

Metaverse has generated significant interest for enabling wireless systems due to its self-sustainability and proactive analytic capabilities. It enables the deployment of meta spaces featuring avatars and digital twins for various real-world applications. However, it has become challenging to efficiently deploy meta spaces on edge/cloud environments while managing communication and computing resources effectively. In this paper, we propose a novel network virtualization framework within a shared system to make the deployment of meta spaces for various metaverse applications cost-efficient. The framework abstracts, isolates, and facilitates the sharing of wireless and computing resources, thereby enhancing flexibility and efficiency for metaverse-driven applications. It involves three key players: network operators (selling resources), metaverse operators (managing transactions), and end-users (purchasing resources). An optimization problem is formulated to optimize the wireless resource allocation, computing resource allocation, association of end-devices with meta spaces deployed at the network operators' base stations, communication resource cost, and computing resource cost. We address the formulated NP-Hard mixed integer non-linear programming (MINLP) problem using decomposition, convex optimization, and hierarchical matching. Numerical results confirm the validity of the proposed approach.

The broadcast nature of wireless channels makes sixth-generation (6 G) networks vulnerable to eavesdropping, necessitating robust physical layer security (PLS). In this paper, we propose a secure non-orthogonal multiple access (NOMA)-enabled unmanned aerial vehicle (UAV)-mounted reconfigurable intelligent surface (RIS) architecture to maximize secrecy rates in the presence of eavesdroppers. UAV-mounted RIS enables dynamic repositioning to establish favorable line-of-sight (LoS) links while suppressing signal leakage. However, jointly optimizing UAV 3D trajectory, RIS phase shifts control, base station (BS) active beamforming, and power allocation is challenging due to tightly coupled variables, strict successive interference cancellation (SIC) constraints, and minimum secrecy requirements. We formulate a secrecy rate maximization problem as a highly non-convex mixed-integer nonlinear program (NC-MINLP), intractable for conventional methods. We reformulate it as a Markov decision process (MDP) and propose a multi-agent multi-stage curriculum learning framework with proximal policy optimization (MAMSCL-PPO). The framework progressively expands the secured user set, addressing reward sparsity and enabling knowledge transfer. Two cooperative agents, employing centralized training with decentralized execution (CTDE), jointly control the UAV trajectory with RIS phase shifts and the BS beamforming with power allocation. Simulations demonstrate that MAMSCL-PPO achieves a 43% higher sum secrecy rate and faster convergence than single-stage training, validating its effectiveness for scalable PLS in NOMA-enabled aerial networks.

The advent of 6G networks promises transformative advances in communication technologies, ushering in an era of unprecedented connectivity, low-latency communication, and ubiquitous computing. However, one of the key challenges in realizing the full potential of 6G lies in efficiently managing and trading resources across diverse domains in terrestrial and non-terrestrial integrated networks (TN-NTN). In this paper, we introduce a novel multi-domain resource trading approach that integrates blockchain technology, hierarchical multi-agent deep reinforcement learning (HMADRL), and multi-leader multi-follower (MLMF) Stackelberg games to address this challenge. HMADRL provides an intelligent and adaptable framework for resource requesters and provider agents to learn optimal resource allocation methods in multiple domains. The MLMF Stackelberg game framework represents the strategic interactions between virtual service providers (SPs), network operators, and computing resource providers to enable them to make dynamic resource allocation and pricing decisions. This hierarchical decision-making strategy ensures a structured bargaining mechanism where leaders optimize their revenue by setting resource pricing and followers strategically decide on their resource-buying strategies. In the proposed scheme, the blockchain includes smart contracts that autonomously execute resource trading agreements, ensuring that transactions are tamper-proof and executed according to predefined rules. The proposed method optimizes the system’s utility by dynamically adjusting the pricing strategy to changing resource demands. The simulation results show that our proposed HMADRL algorithm increases the utility by 19.645%, 33.018%, and 48.791% compared to the modified multi-agent deep deterministic policy gradient (MADDPG), MADDPG, and DDPG algorithms, respectively.

This study examined the role of independent and interdependent self-construal in predicting cybersecurity behaviors and perceived vulnerability across two culturally distinct populations: the UK and Arab countries. 642 adults (314 from the UK and 328 from Arab countries) participated in the survey. Our findings provide novel perspectives into the relationship between self-construal and cybersecurity risk. Individuals with an interdependent self-construal reported feeling more vulnerable to scams and cyberattacks, broadening the understanding of perceived vulnerability beyond basic cultural distinctions. In the Arab sample, independent and interdependent self-construal were significant predictors of cybersecurity behaviors, whereas, in the UK sample, this was predominantly associated with independence. These results highlight the crucial role of the cultural context in shaping the influence of self-construal, challenging the traditional binary framework of individualism versus collectivism applied in cybersecurity research. Furthermore, security attitudes mediated the relationship between interdependent self-construal and vulnerability perceptions in the Arab sample. Still, no mediation was found in the case of independent self-construal across either group. Our findings suggest that self-construal operates differently across cultures, calling for a more nuanced understanding of its role in cybersecurity behaviors. Recognizing these varying self-construal dimensions can help shape more effective, culturally tailored strategies to enhance cybersecurity awareness and resilience in diverse populations.

Amidst growing environmental concerns and the push for sustainability, the intricacies of energy management in diverse building settings demand innovative solutions. Traditional strategies fail to address the unique characteristics and needs of different building types and users leading to inefficiencies and a lack of optimization in energy use. This study presents a sophisticated building energy management system, assessed via pilot implementations, which utilizes a platform based on adaptive and intelligent edge computing. This comprehensive system integrates various elements such as smart meters, HVAC controls, electric vehicle charging stations, and smart plugs. Utilizing the ICT PSP framework, the methodology encompasses rigorous data collection and analysis, emphasizing usability, error rectification, and the detailed monitoring of impacts. Key findings reveal that tailored energy management solutions, augmented by real-time data and user-centric interfaces, significantly improve energy efficiency. The study also highlighted the impact of socio-economic factors and weather conditions on energy consumption, underscoring the necessity of incorporating these variables into energy management strategies. The integration of demand-side management (DSM) through energy retailers, distribution system operators (DSO), or other power grid stakeholders has yielded valuable insights into external factors affecting energy consumption patterns. Specifically, these insights pertain to electricity tariffs and consumer behavior.

Smart building energy demand response (DR) plays a paramount role in the decarbonization of energy use by leveraging different techniques that adjust energy consumption in response to fluctuating prices and demands. In this paper, we present an explainable AI-integrated DR optimization framework for smart buildings. This adaptive XAI-based DR approach intelligently forecasts energy demands and schedules the energy consumption of the buildings based on the forecasted demand patterns, grid conditions, and energy prices. This approach allows the stakeholders to make informed and optimal DR decisions based on the forecasted demand to maximize energy efficiency and user comfort while minimizing electricity costs. The simulation results demonstrate that the proposed approach promotes energy efficiency and user comfort.

In the dynamic vehicular metaverse, delivering a seamless user experience (UX) and effective human-machine interaction (HMI) is challenging due to vehicle mobility and varying resource needs. This paper introduces an adaptive resource allocation and twin migration framework using Multi-Agent Deep Reinforcement Learning (MADRL) for a multi-tier vehicular metaverse. The framework enables cooperative agents to dynamically allocate resources and migrate vehicle twins across vehicle, edge, and cloud layers, ensuring seamless UX and efficient HMI. The joint resource allocation and twin migration optimization problem is modeled as MDP and a hierarchical multi-agent deep deterministic policy gradient-with QMIX (MADDPG-Q) strategy is adopted to solve it, reducing latency and optimizing resource use. Moreover, the proposed framework is designed to be context-aware, adjusting HMI based on real-time conditions, and enhancing interaction quality. Simulation results show significant improvements in UX, latency reduction, and resource efficiency.

Colorectal cancer (CRC) is a widespread critical health issue, and colonoscopy is a well-known standard for colon screening. However, human factors and manual control and report generation limit its effectiveness. We propose a novel framework that incorporates the robotic arm control of the wireless endoscope and utilizes vision language models (VLM) for the generation of automatic colonoscopy reports. The framework integrates deep reinforcement learning (DRL) to optimize frame processing, localize anatomical landmarks, and use VLM to identify abnormalities and produce detailed reports. Many experiments have shown that this framework is promising as it provides low-latency control and streaming while also outperforming existing approaches by achieving high-performance detection (F1 score of 93. 04%) and providing accurate localization of colon regions (F1 score of 84.64%), as well as a reduction in processed frames by 60.1%. This framework represents a novel solution to automate colonoscopy report generation, increasing diagnostic precision, efficiency, and workload reduction among healthcare professionals.