Intelligent and Converged Networks最新文献

{"title":"Reconfigurable Intelligent Surfaces for 6G: Engineering Challenges and the Road Ahead","authors":"Xianming Zhao;Mengnan Jian;Yijian Chen;Yajun Zhao;Lin Mu","doi":"10.23919/ICN.2025.0004","DOIUrl":"https://doi.org/10.23919/ICN.2025.0004","url":null,"abstract":"Reconfigurable Intelligent Surfaces (RISs) have emerged as a pivotal technology for the Sixth-Generation (6G) communication system, showcasing the ability to configure wireless environment dynamically. Acknowledged as a breakthrough in enhancing network coverage, augmenting system capacity, and facilitating advanced applications such as Integrated Communication and Sensing (ISAC), RISs present a concrete approach to molding the future network evolution. The advancement of RIS technology necessitates a departure from idealistic assumptions and oversimplifications, compelling a progression towards models that more accurately reflect the physical attributes of hardware and the characteristics of propagation. In this paper, we delve into the practical constraints and limitations of current RIS design methodologies, conducting a comprehensive analysis based on the latest technological research advancements and product realizations. Our exploration is broad-ranging, encompassing the engineering challenges of single-point RISs, such as hardware impairments, intricacies of algorithm design, frequency spectrum-specific difficulties. A concentrated discourse is presented on novel near-field channel designs, the restrictions imposed by low-bit quantization, and the intricacies of amplitude-phase correlation constraints. This discussion aims to unearth the challenges, opportunities, and paradigmatic shifts induced by the practical deployment of RISs. The deployment challenges, networking dilemmas, simulation, and product evaluation is provided for RISs in large-scale networks from a broader system perspective. Furthermore, this paper highlights the critical need for accelerated efforts towards the commercialization of RISs. We explore the practical application revolution of RISs, encompassing engineering aspects and standardization processes. Our discussion aims to establish a foundational framework for introducing RISs into the market, acknowledging their significant potential as a game-changing technology in 6G communications.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 1","pages":"53-81"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10949779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DSTL: A Dual-Step Transfer Learning-Based Prediction Model for Next-Generation Intelligent Cellular Networks","authors":"Waqar A. Aziz;Iacovos I. Ioannou;Marios Lestas;Vasos Vassiliou","doi":"10.23919/ICN.2025.0005","DOIUrl":"https://doi.org/10.23919/ICN.2025.0005","url":null,"abstract":"Traffic modeling and prediction are indispensable to future extensive data-driven automated intelligent cellular networks. It contributes to proactive and autonomic network control operations within cellular networks. Current methodologies typically rely on established prediction models designed for univariate and multivariate time series forecasting. However, these approaches often demand a substantial volume of training data and extensive computational resources for prediction model training. In this study, we introduce a dual-step transfer learning (DSTL)-based prediction model specifically designed for the prediction of multivariate spatio-temporal cellular traffic. This technique involves the categorization of gNodeBs (gNBs) into distinct clusters based on their traffic pattern correlations. Instead of training the prediction model individually on each gNB, a base model is trained on the aggregated dataset of all the gNBs within a base cluster using a combination of recurrent neural network (RNN) and bidirectional long-short term memory (RNN-BLSTM) network. In the first-step transfer learning (TL), the base model is provided to the gNBs within the base cluster and to the other clusters, where it undergoes the process of fine-tuning the intra-cluster aggregated dataset. Once the model is trained on the aggregated dataset within each cluster, it is provided to the gNBs within the respective cluster in the second-step TL. The model received by each gNB through the proposed DSTL technique either necessitates minimal fine-tuning or, in some cases, requires no further adjustment. We conduct extensive experiments on a real-world Telecom Italia cellular traffic dataset. The results demonstrate that the proposed DSTL-based prediction model achieves a mean absolute percentage error of 2.97%, 9.85%, and 9.73% in predicting spatio-temporal Internet, calling, and messaging traffic, respectively, while utilizing less computational resources and requiring less training time than traditional model training and TL techniques.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 1","pages":"82-101"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10949780","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Interpolated Quantized Guard Band Algorithm for Physical Layer Key Generation","authors":"Yongli An;Kun Zha;Wenfeng Song;Zhanlin Ji","doi":"10.23919/ICN.2025.0006","DOIUrl":"https://doi.org/10.23919/ICN.2025.0006","url":null,"abstract":"With the continuous progress of communication technology, traditional encryption algorithms cannot meet the demands of modern wireless communication security. Secure communication based on physical layer encryption emerges as a solution. To meet the low Key Disagreement Rate (KDR) and high Key Generation Rate (KGR) requirements for physical layer key generation, this paper proposes two quantization algorithms, Improve-CQG and Interpolate-CQG, based on the Channel Quantization with Guard band (CQG) algorithm. The former divides the characteristic quantization into two phases: threshold filtering and guard band quantization, while the latter adds a step after these two phases: interpolation quantization. Compared to the CQG algorithm, the Improve-CQG algorithm enhances the granularity of filtered quantization values. The core concept of the Interpolate-CQG algorithm is to utilize threshold filtering and the rounded-off quantization values from the guard band quantization phase. The symbol information corresponding to these index values is replaced by a new interpolated symbol and inserted into the key by the agreed quantized coordinates. Simulation proves that the Interpolate-CQG is an effective quantization algorithm for the key generation with lower KDR and higher KGR than the Improve-CQA and Improve-CQG.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 1","pages":"102-114"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10949802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Digital Twin Technology for Future 6G","authors":"Lanlan Li","doi":"10.23919/ICN.2025.0002","DOIUrl":"https://doi.org/10.23919/ICN.2025.0002","url":null,"abstract":"With advancement in digitalization and simulation technology, digital twin (DT) technology has emerged as a focal point of research in various industries. In response to the demands for high-quality and efficient wireless communication, it is crucial to conduct an in-depth study of core aspects and key technologies of digital twin technology. This will facilitate a better understanding and exploration of the future development direction of related digital simulation technology within the communication field. This paper provides a systematic summary and analysis of the concept of digital twins, their key technologies, and the current research landscape. Additionally, it explores the research and application fields, as well as the development prospects of digital twin technology in communications. The paper also examines diverse applications of digital twin technology in future 6th generation (6G) networks, including an end-to-end digital twin network architecture framework for non-terrestrial networks (NTNs) in the context of 6G. Finally, it discusses the challenges and opportunities for the widespread implementation of digital twins in future wireless communication networks.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 1","pages":"20-40"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10949695","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cooperative UAV Clustering for Fair Coverage of Communication Regions","authors":"Jiehong Wu;Linpeng Gu;Zhongli Jia;Jinsong Wu","doi":"10.23919/ICN.2025.0001","DOIUrl":"https://doi.org/10.23919/ICN.2025.0001","url":null,"abstract":"Cooperative unmanned aerial vehicles (UAVs) cluster technology is considered a prospective solution for area coverage problems, enabling network access and emergency communications in remote areas. In this paper, we investigate how to control UAV cluster to achieve long-term and stable regional coverage while maintaining link connectivity and minimizing energy consumption, given the limited communication range and energy consumption of the UAVs themselves. To this end, we propose a cooperative UAV cluster strategy based on multi-agent deep reinforcement learning (MADRL) to achieve fair coverage of communication regions, which we call MADRL-based cooperative UAV cluster strategy (MADRL-CUCS). Our solution is a centralized training distributed execution architecture and defines a cluster structure for leader UAVs and follower UAVs. Under the premise of comprehensively considering the maximum coverage, we use a new energy efficiency function to minimize energy consumption, so as to extend the network lifetime of the UAVs cluster networks. The new fairness index and collision avoidance factor are used to ensure that the UAV cluster achieve effective and secure regional coverage. We adopt depth first search algorithm to check the link connectivity of the UAVs during the coverage process. Experiments show that the MADRL-CUCS algorithm outperforms the benchmark algorithm.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 1","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10949778","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Throughput and Priority Optimization Strategy for High Density Healthcare IoT","authors":"Zhenlang Su;Junyu Ren;Yeheng Huang;Yang Liao;Tuanfa Qin","doi":"10.23919/ICN.2025.0003","DOIUrl":"https://doi.org/10.23919/ICN.2025.0003","url":null,"abstract":"In the field of wireless body area networks (WBANs), for solving the complex interference problem of inter-WBANs, a density-based adaptive optimization strategy (DAOS) is proposed in this paper. Firstly, the complex interference problem among WBANs is converted into a distance-based graph coloring model, then time division multiple access and a two-level split clustering methods are adopted to allocate initial time slots for nodes. Secondly, the particle swarm optimization algorithm is used to optimize the time slot of each node for maximizing the throughput. We simulate the scenario on MATLAB simulator. Experimental results show that compared with the traditional scheme in high-density healthcare Internet of Things (IoT) scenarios, DAOS has obvious advantages compared with three comparison strategies of faster convergence rate of 48.94%, 60.76%, and 96.82%, and higher throughput of 5.60%, 8.08%, and 8.05% in traffic priorities 7 to 4.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 1","pages":"41-52"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10949781","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing Darknet Traffic Through Machine Learning and Neucube Spiking Neural Networks","authors":"Iman Akour;Mohammad Alauthman;Khalid M. O. Nahar;Ammar Almomani;Brij B. Gupta","doi":"10.23919/ICN.2024.0022","DOIUrl":"https://doi.org/10.23919/ICN.2024.0022","url":null,"abstract":"The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication channels. Effective detection of clandestine darknet traffic is therefore critical yet immensely challenging. This research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen cybersecurity. Combining diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed threats. Evaluation on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98% accuracy from the random forest model and 84.31% accuracy from the spiking neural network. This pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet communication. The proposed techniques lay the groundwork for improved threat intelligence, real-time monitoring, and resilient cyber defense systems against the evolving landscape of cyber threats.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 4","pages":"265-283"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820902","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Reposition Algorithm for E-Hailing Based on Quantum Annealing and Intuitive Reasoning","authors":"Chao Wang;Yiyun Shi;Sumin Wang","doi":"10.23919/ICN.2024.0020","DOIUrl":"https://doi.org/10.23919/ICN.2024.0020","url":null,"abstract":"Currently, the challenge lies in the traditional intelligent algorithm's ability to effectively address the e-hailing repositioning issue. Accurately identifying the underlying characteristics in extensive traffic data within a limited timeframe is difficult, ultimately preventing the achievement of the most optimal solution. This paper suggests a hybrid computing architecture involving reinforcement learning and quantum annealing based on intuitive reasoning. Intuitive reasoning aims to enhance performance in scenarios with poor system robustness, complex tasks, and diverse goals. A deep learning model is constructed, trained to extract scene features, and combined with expert knowledge, then transformed into a quantum annealable form. The final strategy is obtained using a D-wave quantum computer with quantum tunneling effect, which helps in finding optimal solutions by jumping out of local suboptimal solutions. Based on 400 000 real data, four algorithms are compared: minimum-cost flow, sequential markov decision process, hot-dot strategy, and driver-prefer strategy. The average total revenue increases by about 10% and vehicle utilization by about 15% in various scenarios. In summary, the proposed architecture effectively solves the e-hailing reposition problem, offering new directions for robust artificial intelligence in big data decision problems.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 4","pages":"317-335"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Resource Allocation for D2D-Enabled Social IoT Networks: A Tripartite and Time-Scale Optimization Approach","authors":"Saurabh Chandra;Rajeev Arya;Maheshwari Prasad Singh","doi":"10.23919/ICN.2024.0030","DOIUrl":"https://doi.org/10.23919/ICN.2024.0030","url":null,"abstract":"In the densification of Device-to-Device (D2D)-enabled Social Internet of Things (SIoT) networks, improper allocation of resources can lead to high interference, increased signaling overhead, latency, and disruption of Channel State Information (CSI). In this paper, we formulate the problem of sum throughput maximization as a Mixed Integer Non-Linear Programming (MINLP) problem. The problem is solved in two stages: a tripartite graph-based resource allocation stage and a time-scale optimization stage. The proposed approach prioritizes maintaining Quality of Service (QoS) and resource allocation to minimize power consumption while maximizing sum throughput. Simulated results demonstrate the superiority of the proposed algorithm over standard benchmark schemes. Validation of the proposed algorithm using performance parameters such as sum throughput shows improvements ranging from 17% to 93%. Additionally, the average time to deliver resources to CSI users is minimized by 60.83% through optimal power usage. This approach ensures QoS requirements are met, reduces system signaling overhead, and significantly increases D2D sum throughput compared to the state-of-the-art schemes. The proposed methodology may be well-suited to address the challenges SIoT applications, such as home automation and higher education systems.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 4","pages":"380-401"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820903","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Total Contents","authors":"","doi":"10.23919/TUP-ICN.2024.10820900","DOIUrl":"https://doi.org/10.23919/TUP-ICN.2024.10820900","url":null,"abstract":"","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 4","pages":"I-II"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820900","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}