Design and Implementation of Intelligent Digital Media Interaction System Based on 6G Network Slicing

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Network Management Pub Date : 2025-02-27 DOI:10.1002/nem.70011
Na Liu
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Abstract

Rapid growth in intelligent digital media interaction systems (IDMIS) has created new difficulties in controlling and optimizing content distribution and engagement, especially with the impending 6G networks. The purpose of the investigate is to create an intelligent system that uses 6G network slicing to increase digital media communication and user experience through seamless connectivity, dynamic content distribution, and real-time engagement. The structure includes a dynamic, multilayered architecture for IDMIS, and network capital is allocated through 6G network slicing based on user demand and content type. The system includes machine learning (ML) algorithms that predict user behavior and optimize media delivery in real time. To correctly predict user behavior, the research gathers data that capture users' performance and preference (historical interaction data, demographics, contextual data, and user feedback). Once collected, data are processed to reduce dimensionality using principal component analysis (PCA). Refined Support Vector Machine Integrated with Flying Fox Optimization (RSVM-FFO) predicts user behavior and optimizes media delivery in real time. Metrics are used to evaluate the RSVM-FFO approach, such as F1-score (98.12%), accuracy (98.59%), precision (98.57%), and recall (98.17%). The results reveal that the suggested systems considerably improve media interaction effectiveness by reducing latency and bandwidth usage while providing a highly responsive user experience. Finally, advancement in the delivery of high-performance, customized media services is the combination of an IDMIS with 6G network slicing.

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基于6G网络切片的智能数字媒体交互系统的设计与实现
智能数字媒体交互系统(IDMIS)的快速发展为控制和优化内容分发和参与带来了新的困难,特别是在即将到来的6G网络下。该调查的目的是创建一个使用6G网络切片的智能系统,通过无缝连接、动态内容分发和实时参与来增加数字媒体通信和用户体验。该结构包括IDMIS的动态、多层架构,并根据用户需求和内容类型通过6G网络切片分配网络资金。该系统包括机器学习(ML)算法,可以预测用户行为并实时优化媒体交付。为了正确预测用户行为,该研究收集了用户表现和偏好的数据(历史交互数据、人口统计数据、上下文数据和用户反馈)。一旦收集到数据,使用主成分分析(PCA)对数据进行处理以降低维数。精细化支持向量机集成飞狐优化(RSVM-FFO)预测用户行为并实时优化媒体交付。采用指标评价RSVM-FFO方法,如f1评分(98.12%)、准确率(98.59%)、精密度(98.57%)和召回率(98.17%)。结果表明,建议的系统通过减少延迟和带宽使用,同时提供高度响应的用户体验,大大提高了媒体交互的有效性。最后,在交付高性能定制媒体服务方面的进步是IDMIS与6G网络切片的结合。
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来源期刊
International Journal of Network Management
International Journal of Network Management COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
5.10
自引率
6.70%
发文量
25
审稿时长
>12 weeks
期刊介绍: Modern computer networks and communication systems are increasing in size, scope, and heterogeneity. The promise of a single end-to-end technology has not been realized and likely never will occur. The decreasing cost of bandwidth is increasing the possible applications of computer networks and communication systems to entirely new domains. Problems in integrating heterogeneous wired and wireless technologies, ensuring security and quality of service, and reliably operating large-scale systems including the inclusion of cloud computing have all emerged as important topics. The one constant is the need for network management. Challenges in network management have never been greater than they are today. The International Journal of Network Management is the forum for researchers, developers, and practitioners in network management to present their work to an international audience. The journal is dedicated to the dissemination of information, which will enable improved management, operation, and maintenance of computer networks and communication systems. The journal is peer reviewed and publishes original papers (both theoretical and experimental) by leading researchers, practitioners, and consultants from universities, research laboratories, and companies around the world. Issues with thematic or guest-edited special topics typically occur several times per year. Topic areas for the journal are largely defined by the taxonomy for network and service management developed by IFIP WG6.6, together with IEEE-CNOM, the IRTF-NMRG and the Emanics Network of Excellence.
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