TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Latif U. Khan, Waseem Ullah, Sami Muhaidat, Mohsen Guizani, Bechir Hamdaoui	Block Successive Upper-Bound Minimization for Resource Scheduling in Wireless Metaverse	2025	Early Access	Metaverse Sensors Costs	In recent years, there has been a rising trend towards emerging applications (e.g., brain-computer interaction and haptics-based autonomous cars) with diverse requirements. To effectively enable these applications via autonomous operation and intelligent analytics, one can use a metaverseFor more details on how a metaverse can enable emerging applications and architecture, please refer to khan2024ametaverse. In a metaverse, we have two spaces: (a) a meta space based on a virtual model that performs analysis and resource management and (b) a physical space comprised of real world entities. A metaverse effectively enables emerging applications by performing three main tasks: (a) distributed learning of metaverse models; (b) instantly serving the end-users; and (c) sensing of the physical environment and sharing it with the meta space for synchronized operation. To perform these tasks, efficient wireless resource management is needed. Therefore, a novel resource scheduling framework for the wireless metaverse to enable various applications is proposed. Our aim is to minimize the cost of learning and sensing in metaverse. Subsequently, we formulate a problem. Meanwhile, the reliability as well as latency constraints of the service-requesting devices/users will be fulfilled. We assign multiple resource blocks to learning and sensing devices/units, whereas we use a concept of puncturing for service-requesting devices/users upon arrival. We use a scheme that is based on block successive upper-bound minimization and convex optimization for solving our formulated problem. At the end, we use empirical cumulative distribution function vs. cost and cost vs. metaverse entities for numerical evaluations.	10.1109/TNSM.2025.3562516
Pedro Martinez-Julia, Ved P. Kafle, Hitoshi Asaeda	Intelligent Network Service Automation: Improving Accuracy and Efficiency in Network Management	2025	Early Access	Telemetry Accuracy Artificial intelligence	Network virtualization offers enormous flexibility to construct network services tailored to the needs of network tenants and users. Network services can be implemented through composition of virtual network functions, which can be deployed in multiple sites, forming multi-site network services. However, network virtualization further induces the increasing complexity of the network, which, in turn, increases the difficulty of error avoidance in management operations. Thus, network management must be automated to tackle such complexity and eliminate errors. In this paper, we propose the intelligent network service automation system (INSA), and we demonstrate its high efficiency and accuracy in automated network service construction and management. INSA is designed to transparently orchestrate all elements of the management system using a control plane based on publish/subscribe messaging, known as a management service bus. This enables INSA to make highly accurate decisions using vast telemetry information without overloading the control plane. Additionally, INSA integrates key enablers such as efficient and scalable control plane networking, telemetry compression, distributed processing, hidden fault detection, and autonomic resource control. We evaluated INSA both theoretically and through a prototype implementation. The former proves our claims regarding the efficiency improvement provided by compression and distribution. The latter showcases INSA management accuracy and performance, and it also demonstrates how INSA manages multi-site network services. The evaluation results show that, in comparison with the state-of-the-art alternatives, INSA uses 15–36% less CPU time, possesses 80% higher scalability, and improves accuracy by 33%.	10.1109/TNSM.2025.3541225
Mohamed Elloumi, Md. Zoheb Hassan, Georges Kaddoum	Spectrum Sharing in Internet-of-Vehicles Networks: Digital Twin-Empowered Proactive Interference Management Approach	2025	Early Access	Resource management Vehicle dynamics Interchannel interference	Internet-of-Vehicles (IoV) is envisioned to connect vehicles with each other, the surrounding environment, and central control centers. Spectrum sharing among active vehicular links is imperative to enhance the utilization of the spectrum licensed to IoV networks. However, co-channel interference among neighboring vehicular communication links poses a fundamental challenge when enabling spectrum sharing in IoV networks. This paper introduces a resource optimization framework, entitled PRISM (Proactive Resource optimization for Interference and Spectrum Management), to mitigate co-channel interference in IoV networks. PRISM proactively allocates resources among a set of Vehicle-to-Infrastructure (V2I) communication links by accurately predicting the links’ positions and multi-path channel gains, thereby preventing outdated resource scheduling in dynamic IoV networks. PRISM is a three-step approach. In the first step, a multi-layer long short-term memory neural network and transfer learning are employed to predict the vehicles’ positions. In the second step, a digital twin network incorporating high-fidelity 3D maps and a ray tracing tool entitled SionnaTM is used to predict the V2I links’ multi-path channel gains. In the third step, a resource allocation algorithm is executed to efficiently determine V2I clusters and their transmit power allocations to maximize the overall system capacity. Simulation results show that PRISM enhances IoV network’s capacity up to 33% compared to non-proactive schemes, as validated through a simulation framework using real-world vehicular mobility traces.	10.1109/TNSM.2025.3541977
Jiajun Chen, Chunqiang Hu, Weihong Sheng, Ruinian Li, Ruifeng Zhao, Jiguo Yu	A Trust-Based Personalized Differential Privacy Guarantees for Online Social Networks	2025	Early Access	Privacy Protection Social networking (online)	Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and policy perspective, and in alignment with the concept of privacy as control, users have the right to control their personal privacy information. However, users often encounter challenges in terms of understanding and effectively managing their privacy settings to align with their specific privacy requirements. To address this issue, in this paper, we incorporate the concept of trust and propose a trust-based personalized differential privacy model for online social networks, denoted as TPDP, which relies on a trusted central server to facilitate its operation. Specifically, when a user requests access to another user’s personal information, the TPDP mechanism provides a privacy response, where the privacy level is determined based on the direct and indirect trust values among users, calculated automatically by the trusted central server. Furthermore, the proposed TPDP model offers user-to-user personalized differential privacy protection from the perspectives of network structures, trust-related factors, and trust propagation patterns. Finally, we validate the model’s feasibility and assess the privacy-utility trade-off, as well as its robustness against attacks, through theoretical analysis and performance evaluation.	10.1109/TNSM.2025.3543844
Wei-Che Chien, Gwanggil Jeon, Hsin-Hung Cho	Multi-Objective Optimization of 3D Cell Deployment in Sustainable B5G/6G Networks: Balancing Performance and Sustainability	2025	Early Access	Base stations 5G mobile communication Wireless communication	In recent years, the exponential increase in mobile and Internet of Things (IoT) data traffic has placed substantial demands on infrastructure for Internet Service Providers (ISPs). To meet these demands sustainably, it is critical to enhance energy efficiency, resource utilization, and cost-effectiveness while reducing the carbon footprint. Simply adding hardware is not a viable solution. This study introduces an innovative approach to 3D cellular deployment in sustainable B5G/6G networks, designed to optimize Quality of Service (QoS) for users and IoT devices. Although 5G/B5G utilizes millimeter waves for high data rate transmission, their limited coverage and susceptibility to interference from buildings pose unique deployment challenges. To address these, we formulate the 3D cellular deployment problem as a Multi-Objective Optimization (MOO) problem and propose an advanced deployment strategy using VA-NSGA-II, a metaheuristic-based algorithm. By factoring in building interference, Received Signal Strength Indicator (RSSI), coverage, deployment cost, and a balance between performance and sustainability, VA-NSGA-II provides an optimal deployment solution. Simulation results demonstrate that VA-NSGA-II achieves effective deployment performance across various building materials, highlighting its adaptability and effectiveness in different environmental scenarios.	10.1109/TNSM.2025.3545622
Kai Peng, Jialu Guo, Hao Wang, Jintao He, Zhiqing Zou, Tianping Deng, Menglan Hu	Delay-Aware Joint Microservice Deployment and Request Routing in Multi-Edge Environments Based on Reinforcement Learning	2025	Early Access	Microservice architectures Routing Cloud computing	The service modules of the traditional Mobile Edge Computing (MEC) are difficult to deploy, extend, and maintain in real networks because of the highly sophisticated systems. To promote the generalization, openness, and flexibility of the network edge environment, an increasing number of studies are exploring the integration of microservices with MEC. However, the existing work usually treats microservice deployment and request routing as two separate issues, ignoring the interaction between them. Therefore, this paper focuses on the joint optimization of microservice deployment and request routing in the multi-edge cloud scenarios. We establish a problem model for minimizing the average response latency, considering the transmission of requests across edge clouds. Then, in view of the complexity of the scene, this paper proposes a joint training strategy of microservice deployment and request routing based on deep reinforcement learning and Best Fit Decreasing algorithm. The algorithm takes the change of microservice deployment scheme as the action of the agent, introduces the Best Fit Decreasing algorithm to construct request routing based on the deployment scheme, and calculates rewards using the complete joint microservice deployment and request routing scheme for subsequent network training. Finally, experimental results show that the proposed algorithm can effectively reduce the response time delay and system running power compared with other algorithms.	10.1109/TNSM.2025.3543568
Takanori Hara, Masahiro Sasabe	eBPF-Based Ordered Proof of Transit for Trustworthy Service Function Chaining	2025	Early Access	Security Routing Kernel	Service function chaining (SFC) establishes a service path where a sequence of functions is executed according to service requirements. However, SFC lacks a mechanism to ensure proper traversal of relay nodes in the data plane. Misconfigurations and the presence of attackers can lead to forwarding anomalies and path deviation, potentially allowing packets to bypass security network functions in the service path. To mitigate potential security breaches, ordered proof of transit (OPoT) has been proposed as a mechanism to verify whether traffic adheres to the designated path. In this paper, we realize lightweight OPoT-based path verification based on extended Berkeley Packet Filter (eBPF) for trustworthy SFC. Furthermore, by integrating it with the existing SFC proxy, we extend the proposed approach to accommodate both SFC-aware and SFC-unaware virtual network functions (VNFs) in the segment routing over IPv6 data plane (SRv6) domain. Through experiments, we demonstrate the capability of the proposed approach to detect path deviations. Additionally, we reveal the performance limitations of the proposed approach.	10.1109/TNSM.2025.3550333
Luqi Wang, Shanchen Pang, Haiyuan Gui, Xiao He, Nuanlai Wang, Sibo Qiao, Zhiyuan Zhao	Sustainable Energy-Efficient Multi-Objective Task Processing Based on Edge Computing	2025	Early Access	Servers Energy efficiency Data privacy	As smart cities evolve, rising computational demands strain infrastructures. Offloading tasks to edge cloud data centers offers potential but faces challenges like high latency, energy use, and data leakage, especially in dense urban areas. This paper presents a low-latency, energy-efficient digital twin (DT) architecture tailored for smart cities, integrating edge computing (EC) and multiple intelligent reflective surfaces (IRS) to enhance communication. Dynamic voltage and frequency scaling (DVFS) technology is considered for user devices to reduce energy consumption. To mitigate the risk of user privacy leakage during task offloading, we address sensitive user location data that may be exposed by proposing a perturbed sliding task queue (PSTQ) algorithm based on differential privacy (DP), and demonstrate the effectiveness of the algorithm. To optimize task processing time and energy efficiency, we decompose the complex problem using block coordinate descent and propose an intelligent scheduling for energy sustainability (ISES) algorithm based on Karush-Kuhn-Tucker conditions and deep reinforcement learning (DRL). Experimental results demonstrate that our proposed architecture and algorithms achieve over 90% improvement in key optimization objectives, alleviating the computational pressure on existing devices while significantly enhancing task processing efficiency and energy sustainability.	10.1109/TNSM.2025.3553259
Dahina Koulougli, Kim Khoa Nguyen, Mohamed Cheriet	Cost Optimization Of FlexEthernet Over Elastic Optical Network Fronthaul Design	2025	Early Access	5G mobile communication Uncertainty Computer architecture	Without network slicing supports, traditional Fronthaul architectures struggle to meet the demanding requirements of 5G networks, such as the ultra-low latency and high bit rate specified by the enhanced common public radio interface (eCPRI). In this paper, we design a novel Fronthaul architecture that leverages FlexEthernet (FlexE) over elastic optical network (EON) to enable Fronthaul slicing meeting 5G Fronthaul requirements. Our Fronthaul design is optimized by an integer linear programming (ILP) model, named eFFP, that minimizes the total cost of ownership (TCO). While eFFP meets the strict Fronthaul requirements by provisioning network resources based on worst-case traffic load, it tends to overestimate required bit rate as a result of the inherent uncertainty and variability in real-world traffic. To tackle this challenge, we introduce uFFP, a stochastic Fronthaul provisioning strategy tailored to accommodate uncertain traffic demands and mitigate expenditure wastage. Relying on historical data, uFFP assesses statistical characteristics of traffic patterns to better estimate Fronthaul bit rate. Subsequently, we employ chance-constrained optimization to reformulate the uFFP problem, which is approximately solved using a convex relaxation approach known as uFFPA, and optimally solved using a deep reinforcement learning (DRL) approach called uFFPL. Simulation results demonstrate that our proposed solutions achieve significant cost savings, reducing TCO by 39.79% compared to the baseline.	10.1109/TNSM.2025.3553417
Milan Groshev, Lanfranco Zanzi, Carmen Delgado, Xi Li, Antonio de la Oliva, Xavier Costa-Pérez	Energy-Aware Joint Orchestration of 5G and Robots: Experimental Testbed and Field Validation	2025	Early Access	Robots Robot kinematics 5G mobile communication	5G mobile networks introduce a new dimension for connecting and operating mobile robots in outdoor environments, leveraging cloud-native and offloading features of 5G networks to enable fully flexible and collaborative cloud robot operations. However, the limited battery life of robots remains a significant obstacle to their effective adoption in real-world exploration scenarios. This paper explores, via field experiments, the potential energy-saving gains of, a joint orchestration of 5G and Robot Operating System (ROS) that coordinates multiple 5G-connected robots both in terms of navigation and sensing, as well as optimizes their cloud-native service resource utilization while minimizing total resource and energy consumption on the robots based on real-time feedback. We designed, implemented and evaluated our proposed in an experimental testbed composed of commercial off-the-shelf robots and a local 5G infrastructure deployed on a campus. The experimental results demonstrated that significantly outperforms state-of-the-art approaches in terms of energy savings by offloading demanding computational tasks to the 5G edge infrastructure and dynamic energy management of on-board sensors (e.g., switching them off when they are not needed). This strategy achieves approximately ~15% energy savings on the robots, thereby extending battery life, which in turn allows for longer operating times and better resource utilization.	10.1109/TNSM.2025.3555126
Jian An, Siyu Tang, Ruyuan Ping, Ran Li, Xin He, Xiaolong Jin	Fed RDP: A Robust Federated Learning Framework for Multi Party Decision-Making	2025	Early Access	Training Federated learning Blockchains	In the field of intelligent manufacturing, the vast and heterogeneous nature of data across different departments poses significant challenges for collaborative decision-making. Direct data sharing often leads to severe privacy and security concerns. Although federated learning offers a promising solution, issues such as participant selection and privacy protection remain inadequately addressed. During model training, it is crucial to minimize the influence of low-quality participants and prevent the inference of sensitive information through gradient analysis, which could threaten model performance or data privacy. To address these challenges, this paper proposes a robust federated learning model, Fed-RDP, based on participant contribution evaluation and personalized differential privacy. The model leverages blockchain technology for secure data flow and storage, implemented through smart contracts. Historical parameters are submitted to evaluate contributions based on the concept of the least core, with real-time updates to reputation scores. When participants upload their local models, the scores are used as weights to aggregate and update the global model. Additionally, personalized differential noise is added to the uploaded gradients based on participant scores, preserving privacy while maximizing the utility of the data. Experimental results demonstrate that this approach effectively identifies low-quality participants, optimizes evaluation time, and protects privacy through personalized differential noise.	10.1109/TNSM.2025.3557850
Qingwei Tang, Wei Sun, Zhi Liu, Yang Xiao, Qiyue Li, Xiaohui Yuan, Qian Zhang	Multi-agent Reinforcement Learning Based Delay and Power Optimization for UAV-WMN Substation Inspection	2025	Early Access	Inspection Network topology Optimization	Unmanned aerial vehicles (UAV), due to their flexibility and extensive coverage, have gradually become essential for substation inspections. Wireless mesh networks (WMN) provide a scalable and resilient network environment for UAVs, where each node can serve as either an access point or a relay point, thereby enhancing the network’s fault tolerance and overall resilience. However, the UAV-WMN combined system is complex and dynamic, facing the challenge of dynamically adjusting node transmission power to minimize end-to-end (E2E) delay while ensuring channel utilization efficiency. Real-time topology changes, high-dimensional state spaces, and large solution spaces make it difficult for traditional algorithms to guarantee convergence and stability. Generic reinforcement learning (RL) methods also struggle with stable convergence. This paper introduces a new Lyapunov function-based proof to address these issues and provide a stable condition for dynamic control strategies. Then, we developed a specialized neural network power controller and combined it with the MATD3 algorithm, effectively enhancing the system’s convergence and E2E performance. Simulation experiments validate the effectiveness of this method and demonstrate its superior performance in complex scenarios compared to other algorithms.	10.1109/TNSM.2025.3558823
Jaime Galán-Jiménez, Marco Polverini, Juan Luis Herrera, Francesco G. Lavacca, Javier Berrocal	ELTO: Energy Efficiency-Load balancing Trade-Off Solution to Handle With Conflicting Metrics in Hybrid IP/SDN Scenarios	2025	Early Access	Switches Energy consumption Energy efficiency	Next-generation applications, marked by their critical nature, need to cope with stringent Quality of Service (QoS) requirements, such as low response time and high throughput. Moreover, the increasing number of devices connected to the Internet and the need to provide a consistent network infrastructure to serve the applications requested by users, open the tradeoff of jointly considering the QoS improvement for such applications and the reduction in the energy consumption of the infrastructure. To address this challenge, this paper proposes ELTO (Energy-Load Trade-Off), a system designed for the joint optimization of energy efficiency and traffic load balancing during the transition from IP networks to Software-Defined Networks (SDN). Leveraging SDN and Network Function Virtualization (NFV) paradigms, ELTO introduces an Integer Linear Programming multi-objective formulation, and a Genetic Algorithm heuristic to tackle the optimization problem in large-scale scenarios. ELTO encompasses a holistic approach to network configuration, including network equipment status and routing, to strike a balance between network traffic load balancing and energy efficiency. Results over realistic topologies show the effectiveness of the proposed solution, outperforming other state-of-the-art approaches, being able to switch off nearly half of the links in the network while also reducing the Maximum Link Utilization.	10.1109/TNSM.2025.3559422
Xiaojie Zhang, Saptarshi Debroy, Peng Wang, Keqin Li	DeepRB: Deep Resource Broker Based on Clustered Federated Learning for Edge Video Analytics	2025	Early Access	Internet of Things Resource management Visual analytics	Edge computing plays a crucial role in large-scale and real-time video analytics for smart cities, particularly in environments with massive machine-type communications (mMTC) among IoT devices. Due to the dynamic nature of mMTC, one of the main challenges is to achieve energy-efficient resource allocation and service placement in resource-constrained edge computing environments. In this paper, we introduce DeepRB, a deep learning-based resource broker framework designed for real-time video analytics in edge-native environments. DeepRB develops a two-stage algorithm to address both resource allocation and service placement efficiently. First, it uses a Residual Multilayer Perceptron ( ResMLP) network to approximate traditional iterative resource allocation policies for IoT devices that frequently transition between active and idle states. Second, for service placement, DeepRB leverages a multi-agent federated deep reinforcement learning (DRL) approach that incorporates clustering and knowledge-aware model aggregation. Through extensive simulations, we demonstrate the effectiveness of DeepRB in improving schedulability and scalability compared to baseline edge resource management algorithms. Our results highlight the potential of DeepRB for optimizing resource allocation and service placement for real-time video analytics in dynamic and resource-constrained edge computing environments.	10.1109/TNSM.2025.3560657
Shashank Motepalli, Hans-Arno Jacobsen	Decentralization in PoS Blockchain Consensus: Quantification and Advancement	2025	Early Access	Consensus protocol Blockchains Measurement	Decentralization is a foundational principle of permissionless blockchains, with consensus mechanisms serving a critical role in its realization. This study quantifies the decentralization of consensus mechanisms in proof-of-stake (PoS) blockchains using a comprehensive set of metrics, including Nakamoto coefficients, Gini, Herfindahl-Hirschman Index (HHI), Shapley values, and Zipf’s coefficient. Our empirical analysis across ten prominent blockchains reveals significant concentration of stake among a few validators, posing challenges to fair consensus. To address this, we introduce two alternative weighting models for PoS consensus: Square Root Stake Weight (SRSW) and Logarithmic Stake Weight (LSW), which adjust validator influence through non-linear transformations. Results demonstrate that SRSW and LSW models improve decentralization metrics by an average of 51% and 132%, respectively, supporting more equitable and resilient blockchain systems.	10.1109/TNSM.2025.3561098
Mahdi Nouri, Sima Sobhi-Givi, Hamid Behroozi, Mahrokh G. Shayesteh, Md. Jalil Piran, Zhiguo Ding	Joint Slice Resource Allocation and Hybrid Beamforming with Deep Reinforcement Learning for NOMA based Vehicular 6G Communications	2025	Early Access	Ultra reliable low latency communication Resource management NOMA	The escalating demand for high data rates and dependable communications in forthcoming wireless networks has led to the exploration of innovative solutions. Among these, the fusion of millimeter-wave (mmWave) communications and non-orthogonal multiple access (NOMA) holds significant promise. This paper delves into the optimization of hybrid mmWave-NOMA networks coexisting with Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications, accommodating ultra-reliable low-latency communication (uRLLC) and enhanced mobile broadband (eMBB) services. Our aim is to maximize system spectral efficiency (SE) and energy efficiency (EE) while ensuring fairness among users in terms of signal-to-leakage-and-noise ratio (SLNR). To tackle the intricate optimization challenges, we decompose them into two sequential sub-problems: power allocation and beamforming design, alongside resource block (RB) allocation. We advocate the application of deep reinforcement learning (DRL) algorithms to jointly optimize these sub-problems. Extensive evaluations demonstrate that RL-based approaches effectively enhance SE, EE, fairness, and resource utilization in the mmWave-NOMA network.	10.1109/TNSM.2025.3561251
Mani Shekhar Gupta, Akanksha Srivastava, Krishan Kumar	PORA: A Proactive Optimal Resource Allocation Framework for Spectrum Management in Cognitive Radio Networks	2025	Early Access	Resource management Switches Sensors	Cognitive radio network with proactive resource allocation to identify unused spectrum bands and utilize them opportunistically is observed as an evolving technology to handle spectrum scarcity problem. However, it is a challenging problem to predict the accurate information about availability of unused resources due to randomness in licenced user appearance and high mobility at the cost of minimizing sensing time. To address this issue, we mathematically model the metrics like resource availability probability, resource allocation time, throughput, connection continuance probability, and the expected number of networks switching to propose a proactive optimal resource allocation (PORA) technique. Furthermore, the performance of proposed PORA technique is analysed under different traffic environments such as low, moderate, and high traffic. The results show that the proposed PORA technique addresses challenges related to providing resource allocation in proactive manner over the traditional techniques.	10.1109/TNSM.2025.3540717
Akio Kawabata, Sanetora Hiragi, Bijoy Chand Chatterjee, Eiji Oki	Distributed Processing Network Design Scheme for Virtual Application Processing Platform	2025	Early Access	Delays Synchronization Servers	Delay-sensitive applications have been provided through a low-delay network utilizing multiple edge clouds. For applications that involve sharing status among multiple users, it is crucial to prevent longer communication delays for users who are farther from the application server compared to those who are closer. To address this issue, this paper proposes a distributed processing network design scheme for virtual processing platforms using low-delay networks and widely distributed servers. The proposed scheme introduces Tapl as a given parameter for correcting events in occurrence order. Events within Tapl delay are sorted in occurrence order. The proposed scheme can change its operation mode from a conservative synchronization to an optimistic synchronization depending on the setting of Tapl. The proposed scheme is formulated as a mixed-integer linear programming problem to determine users’ and servers’ distributed processing network configuration. We evaluate the proposed scheme on two different network topologies. Numerical results indicate that, depending on the setting of Tapl, the proposed scheme can reduce the maximum amount of memory used for rollback processes in optimistic synchronization-based applications or realize a conservative synchronization algorithm. The computation time under the condition of 1000 users is within a maximum of nine sec, an acceptable amount of time for preparation before starting a planned service. These results indicate that the proposed scheme realizes event order correction with excellent delay characteristics and applies to virtual processing platforms.	10.1109/TNSM.2025.3562208
Fangyu Zhang, Yuang Chen, Hancheng Lu, Yongsheng Huang	Network-Aware Reliability Modeling and Optimization for Microservice Placement	2025	Early Access	Reliability Software reliability Microservice architectures	Optimizing microservice placement to enhance the reliability of services is crucial for improving the service level of microservice architecture-based mobile networks and Internet of Things (IoT) networks. Despite extensive research on service reliability, the impact of network load and routing on service reliability remains understudied, leading to suboptimal models and unsatisfactory performance. To address this issue, we propose a novel network-aware service reliability model that effectively captures the correlation between network state changes and reliability. Based on this model, we formulate the microservice placement problem as an integer nonlinear programming problem, aiming to maximize service reliability. Subsequently, a service reliability-aware placement (SRP) algorithm is proposed to solve the problem efficiently. To reduce bandwidth consumption, we further discuss the microservice placement problem with the shared backup path mechanism and propose a placement algorithm based on the SRP algorithm using shared path reliability calculation, known as the SRP-S algorithm. Extensive simulations demonstrate that the SRP algorithm reduces service failures by up to 22% compared to the benchmark algorithms. By introducing the shared backup path mechanism, the SRP-S algorithm reduces bandwidth consumption by up to 64% compared to the SRP algorithm with the fully protected path mechanism. It also reduces service failures by up to 11% compared to the SRP algorithm with the shared backup mechanism.	10.1109/TNSM.2025.3562913
Yi-Huai Hsu, Chen-Fan Chang, Chao-Hung Lee	A DRL Based Spectrum Sharing Scheme for multi-MNO in 5G and Beyond	2025	Early Access	Resource management 5G mobile communication Long Term Evolution	In spectrum pooling, which is a well-known technique of spectrum sharing, the initial licensed spectrum of each Mobile Network Operator (MNO) is partitioned into reserved and shared spectrum. The reserved spectrum is for the personal use of an MNO, and the shared spectrum of all MNOs constitutes a spectrum pool that can be flexibly utilized by MNOs that require extra spectrum. Nevertheless, the spectrum pool management problem substantially impacts the spectrum efficiency among these MNOs. In this paper, we formulate this problem as a non-linear programming problem that strives to maximize the average binary scale satisfaction (BSS) of MNOs. To achieve this objective, we introduce an event-driven deep reinforcement learning-based spectrum management scheme, termed EDRL-SMS. This approach adopts a spectrum pool manager (SPM) to efficiently supervise the spectrum pool to reach long-term optimization of network performance. The SPM smartly allocates spectrum resources by fully utilizing a DRL approach, Deep Deterministic Policy Gradient, for each stochastic arrival spectrum request event. The simulation results show that the average BSS of MNOs of the proposed EDRL-SMS significantly outperform our previous work, Bankruptcy Game-based Resource Allocation (BGRA), greedy, random, and without sharing schemes.	10.1109/TNSM.2025.3562968