IEEE Transactions on Network and Service Management最新文献

Entity-Level Autoregressive Relational Triple Extraction Toward Knowledge Graph Construction for Network Operation and Maintenance 面向网络运维知识图谱构建的实体级自回归关系三重抽取

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-03-06 DOI: 10.1109/TNSM.2026.3671463

Yuanzhen Jiang;Yaqiong Liu;Xidian Wang;Nan Cheng;Zihan Jia;Duo Shi;Zhe Lv;Zhouyuan Li;Yan Zhang

{"title":"Entity-Level Autoregressive Relational Triple Extraction Toward Knowledge Graph Construction for Network Operation and Maintenance","authors":"Yuanzhen Jiang;Yaqiong Liu;Xidian Wang;Nan Cheng;Zihan Jia;Duo Shi;Zhe Lv;Zhouyuan Li;Yan Zhang","doi":"10.1109/TNSM.2026.3671463","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3671463","url":null,"abstract":"With the significant increase of communication network scales, intelligent Network Operation and Maintenance (NOM) becomes essential. Knowledge Graphs (KGs) are a key enabler for intelligent NOM, and Relational Triple Extraction (RTE) plays a critical role in KG construction. However, most existing RTE researches rely on general-domain corpora, with limited exploration into the specialized domain. In this paper, we identify a novel challenge in Chinese NOM corpus — Segmented Entity, which has garnered little attention in prior works. To address it, this paper proposes an Entity-level Autoregressive RTE (EARTE) method, which incorporates an innovative Segmented-BIO (Begin, Inside, Outside) tagging scheme. Furthermore, we construct the CMIM23-NOM1-RA, the first high-quality restricted domain RTE dataset for NOM. Throughout the experimentation, we meticulously reproduce all baselines and provide a comprehensive analysis. The results show that EARTE achieves the best performance on CMIM23-NOM1-RA. EARTE’s F1 scores surpass those of the best-performing baselines by 0.4%, 2.7%, and 0.8% under the strict criterion, the lenient criterion, and the setting focusing only on segmented entities, respectively. Finally, our codes, dataset, and reproduction guidelines are publicly available at: <uri>https://github.com/JYzzzzzz/PEAR-RTE</uri>","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"3026-3038"},"PeriodicalIF":5.4,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BiTrustChain: A Dual-Blockchain Empowered Dynamic Vehicle Trust Management for Malicious Detection in IoV BiTrustChain：基于双区块链的车联网恶意检测动态车辆信任管理

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-03-04 DOI: 10.1109/TNSM.2026.3670385

Honghao Gao;Qionghuizi Ran;Ye Wang;Yueshen Xu

{"title":"BiTrustChain: A Dual-Blockchain Empowered Dynamic Vehicle Trust Management for Malicious Detection in IoV","authors":"Honghao Gao;Qionghuizi Ran;Ye Wang;Yueshen Xu","doi":"10.1109/TNSM.2026.3670385","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3670385","url":null,"abstract":"The rapid development of the Internet of Vehicles (IoV) has accelerated technological progress, but several critical security challenges remain, especially in the context of vehicle trust management. Two representative issues are malicious nodes and unreliable information transmission. To address these problems, we propose BiTrustChain, a dual-layer blockchain framework designed to enhance security and trust management in IoV environments. First, it consists of two innovative data chains: a Behavior Data Chain (BDC) and a Reputation Evaluation Chain (REC). The BDC records vehicle interaction data, whereas the REC stores and updates the trust values in real time. Second, within this framework, we develop a Multifactor Bayesian Reputation (MFBR) model that enables quantitative evaluation of node trustworthiness. It integrates a time-decay function and a penalty mechanism to regulate reputation evolution. The trust values decrease after malicious behaviors and recover through continuous normal interactions. In addition, we propose a dynamic local access-list for indirect reputation evaluation. It filters out untrustworthy nodes and ensures that only reliable nodes remain. The filtered indirect trust is then combined with direct trust to produce a comprehensive reputation score. Third, we design a new set of event-driven smart contracts to synchronize the BDC and REC in real time and ensure secure and efficient data exchange. Finally, we performed experiments on the evaluation platform SUMO/NS-3, and the results show that our method identifies malicious nodes with higher accuracy. In particular, the framework achieves <inline-formula> <tex-math>$1.5times $ </tex-math></inline-formula> higher throughput and reduces latency by 40% compared to the baseline single-chain system. The framework also enhances interaction data integrity and improves robustness against adversarial reputation manipulation.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"3039-3054"},"PeriodicalIF":5.4,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A UAV-Aided Digital Twin Framework for IoT Networks With High Accuracy and Synchronization 高精度、同步的物联网网络无人机辅助数字孪生框架

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-03-03 DOI: 10.1109/TNSM.2026.3670040

Ghofran Khalaf;May Itani;Sanaa Sharafeddine

{"title":"A UAV-Aided Digital Twin Framework for IoT Networks With High Accuracy and Synchronization","authors":"Ghofran Khalaf;May Itani;Sanaa Sharafeddine","doi":"10.1109/TNSM.2026.3670040","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3670040","url":null,"abstract":"Digital Twin (DT) technology has emerged as a promising link between the physical and virtual worlds, enabling simulation, prediction, and real-time performance optimization in different domains. In this work we develop a high-fidelity digital twin framework, focusing on synchronization and accuracy between physical and digital systems to enhance data-driven decision making. To achieve this, we deploy several stationary UAVs in optimized locations to collect data from IoT devices, which were used to monitor multiple physical entities and perform computations to evaluate their status. We formulate a mixed-integer non-convex program to maximize the total amount of data collected from all IoT devices while ensuring a constrained age of digital twin threshold and solve it using successive convex approximation (SCA). To cope with realistic scenarios involving unpredictable environments and large network sizes, we model our problem as a Markov Decision Process (MDP), and propose a deep reinforcement learning-based approach using a Twin Delayed Deep Deterministic Policy Gradient (TD3) to optimize the uncrewed aerial vehicle positions and the sum rate. Finally, we present different simulation results of the SCA and TD3 based solutions together with two baseline approaches and evaluated the sum rate in terms of IoT device count, AoDT threshold, task arrival rate and UAVs’ computational capacity. In all simulation results, the proposed TD3-based approach consistently proved to be superior as compared to the baseline solutions.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"3013-3025"},"PeriodicalIF":5.4,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-Empowered Multivariate Probabilistic Forecasting: A Key Enabler for Sustainability in Open RAN 人工智能支持的多元概率预测：开放式RAN可持续性的关键推动者

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-03-02 DOI: 10.1109/TNSM.2026.3669847

Vaishnavi Kasuluru;Luis Blanco;Cristian J. Vaca-Rubio;Engin Zeydan;Albert Bel

{"title":"AI-Empowered Multivariate Probabilistic Forecasting: A Key Enabler for Sustainability in Open RAN","authors":"Vaishnavi Kasuluru;Luis Blanco;Cristian J. Vaca-Rubio;Engin Zeydan;Albert Bel","doi":"10.1109/TNSM.2026.3669847","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3669847","url":null,"abstract":"This paper explores the role of multivariate probabilistic forecasting in improving O-RAN operations, focusing on network sustainability aspects. A comprehensive analysis of its potential benefits and challenges, as well as its integration into the O-RAN architecture are described. The paper first presents an overview of the O-RAN architecture and components, followed by an examination of power consumption models relevant to O-RAN deployments and the challenges associated with traditional deterministic models in resource allocation. We then examine the performance of several state-of-the-art probabilistic multivariate forecasting techniques namely, Gaussian Process Vector Autoregression (GPVAR), Temporal Fusion Transformer (TFT) and non-probabilistic multivariate technique namely, Multivariate Long-Short Term Memory (LSTM) and explain their implementation details and provide their evaluations. The simulation results show the effectiveness of these techniques in predicting Physical Resource Block (PRB) utilization and optimizing resource allocation. In particular, significant energy savings –around 20-30%– are achieved, depending on the percentile of the used probabilistic forecasting techniques. The benefits of considering probabilistic forecasting techniques compared to multi-variate LSTM are also analyzed. Our results emphasize the potential of probabilistic forecasting to improve energy efficiency and sustainability in O-RAN operations.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"3071-3087"},"PeriodicalIF":5.4,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11418715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy-Preserving and Collusion-Resistant Data Query Scheme for Vehicular Platoons 车辆队列的隐私保护和抗合谋数据查询方案

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-27 DOI: 10.1109/TNSM.2026.3669013

Chengyuan Ma;Peng Hu;Tianjiao Ni;Ying Liu;Liangchen Hu;Kaizhong Zuo;Fulong Chen;Yonglong Luo

{"title":"Privacy-Preserving and Collusion-Resistant Data Query Scheme for Vehicular Platoons","authors":"Chengyuan Ma;Peng Hu;Tianjiao Ni;Ying Liu;Liangchen Hu;Kaizhong Zuo;Fulong Chen;Yonglong Luo","doi":"10.1109/TNSM.2026.3669013","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3669013","url":null,"abstract":"Data queries play a crucial role in the vehicular platoon, enabling vehicles to obtain traffic information about surrounding road conditions and personalized entertainment information services. However, data query requests from vehicles may expose the vehicle owner’s personal attributes and habits. Although several schemes can address these issues, they are incapable of countering collusion attacks between the coordinating vehicle and roadside units (RSUs). To solve this problem, in this paper we propose a privacy-preserving and collusion-resistant data query scheme, named PCDQ. Specifically, PCDQ uses the Paillier encryption and Chinese Remainder Theorem to protect the query privacy of vehicle owners, allowing the RSU to recover individual data query requests without associating them with the original vehicles. Next, the parameter update mechanism in PCDQ prevents the coordinating vehicle from obtaining the corresponding mapping information between vehicles and query parameters, thereby resisting collusion attacks between the coordinating vehicle and RSUs. In addition, identity-based signcryption is used to ensure secure parameter distribution among vehicles, and the batch verification enables efficient authentication of query requests. Detailed security proofs and analysis demonstrate that PCDQ satisfies multiple security properties, including resistance to collusion attacks and replay attacks, unlinkability, confidentiality, and authentication and data integrity. Experimental results show that, compared to existing solutions, PCDQ performs better in terms of computation overhead, communication overhead, and network performance.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2852-2865"},"PeriodicalIF":5.4,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent Xn-Based Energy-Aware Handover Optimization in 5G Networks via NWDAF-Orchestrated Agent Framework 基于nwdaf协调Agent框架的5G网络智能xn能量感知切换优化

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-26 DOI: 10.1109/TNSM.2026.3668238

Mahnoor Sajid;Mohib Ullah Khan;KyungHi Chang

{"title":"Intelligent Xn-Based Energy-Aware Handover Optimization in 5G Networks via NWDAF-Orchestrated Agent Framework","authors":"Mahnoor Sajid;Mohib Ullah Khan;KyungHi Chang","doi":"10.1109/TNSM.2026.3668238","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3668238","url":null,"abstract":"Energy-aware mobility management in dense Fifth Generation (5G) networks is increasingly challenged by frequent handovers and unnecessary activation of sleeping next-generation NodeBs (gNBs), which lead to excessive energy consumption and degraded mobility reliability. Conventional Xn-based handover schemes rely on static radio thresholds and implicitly assume always-active gNBs, while existing NWDAF-enabled approaches improve stability but do not explicitly account for energy costs during handover execution. To address these limitations, this paper proposes the Intelligent Energy-Aware Handover Framework (IEAHF), a NWDAF-orchestrated architecture that integrates coordinated agent-based control for energy-aware mobility management. The proposed framework introduces an Energy-Aware Handover Optimization Agent (EA-HOA) to guide reliability-driven handover decisions and a Handover Energy Evaluation Agent (HEEA) to assess the energy impact of candidate handover actions, with both agents operating within a closed-loop control process enforced through Operations, Administration, and Maintenance (OAM). By reformulating handover success to incorporate energy inefficiency and enabling per-handover gNB energy reasoning at decision time, IEAHF jointly optimizes service continuity and energy efficiency. System-level simulations demonstrate that the proposed Agent-NWDAF configuration consistently outperforms baseline and analytics-only schemes, achieving tightly concentrated Energy-Aware Handover Success Rates of approximately 0.98, reducing the Energy-Aware Handover Failure Rate to the range of 0.007–0.015, and delivering up to a 32% reduction in average gNB power consumption relative to an always-on baseline. These results indicate that IEAHF provides a scalable and effective solution for energy-efficient mobility management in 5G networks and establishes a foundation for energy-aware handover control in future Sixth Generation (6G) systems.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2996-3012"},"PeriodicalIF":5.4,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IBNS: Optimizing Intent-Based 6G Network Slicing for Conflict Detection and Mitigation IBNS：优化基于意图的6G网络切片，用于冲突检测和缓解

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-25 DOI: 10.1109/TNSM.2026.3668027

Behnam Ojaghi;Ricard Vilalta;Raül Muñoz

引用次数: 0

End-to-End Deterministic Network Slicing: A Profit-Driven Optimization Framework With Domain-Specific Algorithms 端到端确定性网络切片：具有特定领域算法的利润驱动优化框架

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-25 DOI: 10.1109/TNSM.2026.3667996

Kaili Qian;Yiqin Lu;Xiaohuan Zhang;Wenqi Zhou

{"title":"End-to-End Deterministic Network Slicing: A Profit-Driven Optimization Framework With Domain-Specific Algorithms","authors":"Kaili Qian;Yiqin Lu;Xiaohuan Zhang;Wenqi Zhou","doi":"10.1109/TNSM.2026.3667996","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3667996","url":null,"abstract":"With the rapid evolution of 5G/6G networks, deterministic services such as the Industrial Internet, vehicular communications, and immersive applications impose stringent end-to-end (E2E) latency guarantees. Efficient resource allocation for E2E network slicing (NS) is therefore required to simultaneously satisfy strict latency constraints and improve overall resource efficiency. However, orchestrating cross-domain and multidimensional resources across the radio access network (RAN) and core network (CN) remains challenging due to strong coupling and scalability issues. To address this problem, this paper develops a profit-driven optimization framework for static E2E NS, which jointly accounts for deterministic service guarantees and system profitability. On the RAN side, a hybrid genetic algorithm (HGA) is developed to preserve the global exploration capability of genetic algorithms, while incorporating a gradient-guided resource scaling (GGRS) operator to refine elite solutions and improve solution quality. On the CN side, we design a capacity-aware workload balancing algorithm (CA-WLB). For each candidate path, CA-WLB couples capacity adaptive virtual network function (VNF) placement with demand responsive CPU allocation to jointly optimize latency and resource consumption, and then selects the path that yields the maximum profit. Extensive simulations across a wide range of scenarios demonstrate that the proposed framework consistently outperforms conventional heuristic and meta-heuristic approaches, while achieving performance close to high-complexity optimization benchmarks with significantly reduced computational overhead.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2916-2934"},"PeriodicalIF":5.4,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Normalization TD3-Based Task Offloading for UAV-Assisted Collaborative Computing 基于动态归一化td3的无人机辅助协同计算任务分流

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-24 DOI: 10.1109/TNSM.2026.3667404

Xin Song;Biao Zhang;Ze Fan;Ruomeng Li;Siyang Xu

{"title":"Dynamic Normalization TD3-Based Task Offloading for UAV-Assisted Collaborative Computing","authors":"Xin Song;Biao Zhang;Ze Fan;Ruomeng Li;Siyang Xu","doi":"10.1109/TNSM.2026.3667404","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3667404","url":null,"abstract":"To meet the computational requirements of computation-intensive and delay-sensitive applications, we construct an Unmanned Aerial Vehicle (UAV)-assisted three-layer collaborative computing framework that integrates local, edge, and cloud computing resources. However, in dynamic UAV-assisted environments, some existing approaches lack adaptability and struggle to effectively balance delay and energy consumption. To address these challenges, we formulate a joint optimization problem that minimizes the weighted sum of delay and energy consumption, where adaptive weight factors are dynamically adjusted according to system state variations. Due to the non-convex and high-dimensional nature of our proposed problem, traditional optimization methods are generally inadequate. Hence, the problem is modeled as a Markov Decision Process (MDP), and a normalization-based reward function is designed to eliminate the dimensional imbalance between delay and energy consumption. A Dynamic Normalization Twin Delayed Deep Deterministic Policy Gradient (DN-TD3) algorithm is then proposed, which incorporates mechanisms of adaptive exploration and critic-driven policy updates to enhance convergence stability and reduce sensitivity to hyperparameters. Simulation results demonstrate that the proposed DN-TD3 algorithm outperforms benchmark schemes in terms of system cost reduction, convergence speed, and overall stability.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2762-2777"},"PeriodicalIF":5.4,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mitigating Interferences in 5G O-RAN HetNets Through ML-Driven xAPP to Enhance Users’ QoS 利用机器学习驱动的xAPP减少5G O-RAN网络中的干扰，提高用户的QoS

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-24 DOI: 10.1109/TNSM.2026.3667462

Devanshu Anand;Gabriel-Miro Muntean

{"title":"Mitigating Interferences in 5G O-RAN HetNets Through ML-Driven xAPP to Enhance Users’ QoS","authors":"Devanshu Anand;Gabriel-Miro Muntean","doi":"10.1109/TNSM.2026.3667462","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3667462","url":null,"abstract":"In today’s rapidly evolving telecommunications landscape, the demand for seamless connectivity and top-tier network performance has reached unprecedented levels. Traditional cellular systems, while valiant in their service, now struggle under the weight of spiraling data demands, spectrum scarcity, and power inefficiency. The era of ultra-dense mobile networks, with Heterogeneous Networks (HetNets) at the forefront, ushers in improved throughput, spectral efficiency, and energy management. To tackle these challenges, this paper introduces MLCIMO (Machine Learning-enhanced Classification for Interference Management and Offloading) into 5G HetNets. MLCIMO employs a multi-binary classification strategy to categorize users based on interference types and levels. It also introduces an offloading scheme tailored to user service priorities, enhancing the user quality of experience, while conserving energy. It seamlessly aligns with the evolving needs of the HetNets, addressing some of the issues introduced by small cell deployments. Simulation results show that MLCIMO achieves the highest throughput, shortest delay, and lowest packet loss ratio in comparison with alternative approaches. In a comprehensive analysis, the varying degrees of interference encountered by users under different schemes are unveiled, further establishing MLCIMO’s distinguished position in mitigating interference.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2866-2883"},"PeriodicalIF":5.4,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11408918","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0