改进的深度强化学习与更快的图递归卷积神经网络支持的自适应网络切片,用于下一代网络中的定制服务交付

IF 1.7 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC International Journal of Communication Systems Pub Date : 2024-12-18 DOI:10.1002/dac.6049
S. Sugapriya, R. Vijayabhasker
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引用次数: 0

摘要

下一代无线网络预计将支持许多新的用例和广泛的垂直业务。为了适应不同业务的严格要求,实现了网络切片。该技术通过将基础设施网络划分为多个逻辑网络,实现了基于服务的资源分配。在动态和不可预测的情况下,为端到端(E2E)切片管理跨多个域和维度的资源仍然存在困难。租户满意度要求在收入和资源分配费用之间取得平衡。网络切片已成为下一代网络的基本模式,以满足各种应用和用户的不同业务需求。然而,网络条件和业务需求的动态性对有效分配网络资源以满足性能目标提出了挑战。本文提出了一种改进深度强化学习(IDRL)的更快的图递归卷积神经网络(FGRCNN),除了从网络结构中学习流量行为外,还从链路和节点属性中学习流量行为。为了在IDRL框架中训练FGRCNN模型而不需要标记的训练数据集,可以使用深度q -学习技术。这使得框架能够迅速调整以适应流量动态的变化。提出了一种分析实时网络和业务数据的系统,使网络切片能够动态适应不断变化的流量模式和业务需求。提出了一个综合框架,将深度学习模型与网络切片编排机制集成在一起,以实现量身定制的服务交付。通过大量的仿真和实验,证明了该方法在优化资源利用、提高服务质量和实现下一代网络敏捷网络管理方面的有效性。结果强调了基于深度学习的自适应网络切片在未来网络环境中支持多样化和不断发展的服务需求的潜力。
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Improved Deep Reinforcement Learning With Faster Graph Recurrent Convolutional Neural Network-Enabled Adaptive Network Slicing for Tailored Service Delivery in NextGen Networks

Many new use cases and a broad spectrum of vertical businesses are expected to be supported by next-generation wireless networks. Network slicing has been implemented to suit the stringent requirements of different services. By dividing the infrastructure network into several logical networks, this technology enables resource allocation based on services. In dynamic and unpredictable situations, managing resources across several domains and dimensions for end-to-end (E2E) slicing still presents difficulties. Tenant satisfaction requires striking a balance between the trade-off between revenue and the expense of resource allocation. Network slicing has emerged as a fundamental paradigm in next-generation networks to meet the diverse service requirements of various applications and users. However, the dynamic nature of network conditions and service demands poses challenges in efficiently allocating network resources to meet performance objectives. In this paper, a faster graph recurrent convolutional neural network (FGRCNN) with improved deep reinforcement learning (IDRL) is proposed to learn traffic behavior from link and node properties in addition to network structure. To train the FGRCNN model in the IDRL framework without requiring a labeled training dataset, employ the Deep Q-learning technique. This allows the framework to swiftly adjust to changes in traffic dynamics. A system is proposed to analyze real-time network and service data enabling dynamic adaptation of network slices for changing traffic patterns and service requirements. A comprehensive framework is presented that integrates deep learning models with network slicing orchestration mechanisms to achieve tailored service delivery. Through extensive simulations and experiments, the effectiveness approach in optimizing resource utilization is demonstrated, improving service quality and enabling agile network management in next-gen networks. Results highlight the potential of deep learning-enabled adaptive network slicing to support diverse and evolving service demands in future network environments.

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来源期刊
CiteScore
5.90
自引率
9.50%
发文量
323
审稿时长
7.9 months
期刊介绍: The International Journal of Communication Systems provides a forum for R&D, open to researchers from all types of institutions and organisations worldwide, aimed at the increasingly important area of communication technology. The Journal''s emphasis is particularly on the issues impacting behaviour at the system, service and management levels. Published twelve times a year, it provides coverage of advances that have a significant potential to impact the immense technical and commercial opportunities in the communications sector. The International Journal of Communication Systems strives to select a balance of contributions that promotes technical innovation allied to practical relevance across the range of system types and issues. The Journal addresses both public communication systems (Telecommunication, mobile, Internet, and Cable TV) and private systems (Intranets, enterprise networks, LANs, MANs, WANs). The following key areas and issues are regularly covered: -Transmission/Switching/Distribution technologies (ATM, SDH, TCP/IP, routers, DSL, cable modems, VoD, VoIP, WDM, etc.) -System control, network/service management -Network and Internet protocols and standards -Client-server, distributed and Web-based communication systems -Broadband and multimedia systems and applications, with a focus on increased service variety and interactivity -Trials of advanced systems and services; their implementation and evaluation -Novel concepts and improvements in technique; their theoretical basis and performance analysis using measurement/testing, modelling and simulation -Performance evaluation issues and methods.
期刊最新文献
QDND: Quorum-Based Energy Efficiency Aware Directional Neighbor Discovery in Ad Hoc Millimeter Wave Wireless Networks Energy-Effective Optimal Routing–Driven Hybrid Optimizations–Enabled IoT-Based Wearable Wireless Body Area Network Issue Information On Outage Performance and Ergodic Capacity Analysis of Two User NOMA in a Cooperative Cognitive Radio Network With an Energy Harvesting Relay Indoor Ultrasonic Localization Using Artificial Rabbit Optimization Algorithm and BP Neural Network
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