.png){kind=icon}
返回
Resource multi-objective mapping algorithm based on virtualized network functions: RMMA
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2018.01.028.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Existing radio access network systems are static and rigid; they cannot easily satisfy the increasingly large volume of mobile traffic. A new multi-objective optimization approach was developed to leverage the complexity and scalability of radio resource allocation in large-scale radio access networks. A mathematical model of virtualized resource mapping in a heterogeneous radio access network is proposed in this study. We expanded the dynamic differential evolutionary algorithm by regulating the weight parameters of each objective with machine learning to solve the mathematical model. Our approach is evaluated comprehensively in terms of complexity and convergence, and simulations are conducted to verify the proposed approach and demonstrate that the unilateral value of our multi-objective optimization can mirror the results of single -objective optimizations. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Multi-objective optimization
Virtualization
Heterogeneous radio access networks
Dynamic differential evolution
Mapping
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
An autonomous 16 mm/sup 3/ solar-powered node for distributed wireless sensor networks用于分布式无线传感器网络的自治16毫米/sup3/太阳能节点
PREAI
分享收藏Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
PREAI
分享收藏System Architecture and Key Technologies for 5G Heterogeneous Cloud Radio Access Networks.png)
IEEE NETWORK
OAAI
IEEE NETWORKIF6.3
OAAI分享收藏Characterizing Energy-Delay Tradeoff in Hyper-Cellular Networks With Base Station Sleeping Control
PREAI
分享收藏
查看更多内容