Slice admission control in 5G wireless communication with multi-dimensional state space and distributed action space: A sequential twin actor-critic approach

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Computer Networks Pub Date : 2024-10-29 DOI:10.1016/j.comnet.2024.110878
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Abstract

Network slicing represents a paradigm shift in the way resources are allocated for different 5G network functions through network function virtualization. This innovation aims to facilitate logical resource allocation, accommodating the anticipated surge in network resource requirements. This will harness automatic processing, scheduling, and orchestration for efficient management. To overcome the challenge of managing network resources under heavy demand, slice providers need to leverage both artificial intelligence and slice admission control strategies. While 5G network resources can be allocated to maintain a slice, the logical allocation and real-time network evaluation must be continuously examined and adjusted if network resilience is to be maintained. The complex task of leveraging slice admission control to maintain 5G network resilience has not been fully investigated. To tackle this problem, we propose a machine learning approach for slice admission control and resource allocation optimization so as to maintain network resilience. Machine learning algorithms offer a powerful tool for making robust and autonomous decisions, which are crucial for effective slice admission control. By intelligently allocating resources based on real-time demand and network conditions, these algorithms can help ensure long-term network resilience and achieve key objectives. While various machine learning algorithms hold promise for 5G resource management and admission control, reinforcement learning (RL) has emerged as a particularly exciting solution. Its ability to mimic human learning processes makes it a versatile solution, well-suited to tackle the complex challenges of network control. To fill this gap, we propose a new technique known as sequential twin actor critic (STAC). Simulations show that the STAC improves network resilience through enhanced admission probability and overall utility.
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具有多维状态空间和分布式行动空间的 5G 无线通信中的分片准入控制:顺序孪生行为批评方法
网络切片是通过网络功能虚拟化为不同 5G 网络功能分配资源的一种模式转变。这一创新旨在促进合理的资源分配,满足预期激增的网络资源需求。这将利用自动处理、调度和协调来实现高效管理。为了克服在大量需求下管理网络资源的挑战,切片提供商需要利用人工智能和切片准入控制策略。虽然可以分配 5G 网络资源来维护分片,但如果要保持网络弹性,就必须不断检查和调整逻辑分配和实时网络评估。利用切片准入控制来保持 5G 网络弹性这一复杂任务尚未得到充分研究。为解决这一问题,我们提出了一种机器学习方法,用于切片准入控制和资源分配优化,以保持网络弹性。机器学习算法为做出稳健、自主的决策提供了强有力的工具,而这对有效的切片准入控制至关重要。通过根据实时需求和网络条件智能分配资源,这些算法有助于确保网络的长期弹性并实现关键目标。虽然各种机器学习算法在 5G 资源管理和接入控制方面大有可为,但强化学习(RL)已成为一种特别令人兴奋的解决方案。强化学习能够模仿人类的学习过程,因此是一种多用途解决方案,非常适合应对网络控制的复杂挑战。为了填补这一空白,我们提出了一种被称为 "连续双行动者批判者(STAC)"的新技术。仿真结果表明,STAC 可通过提高接纳概率和整体效用来改善网络弹性。
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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
期刊最新文献
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