Provisioning of 5G Services Employing Machine Learning Techniques

被引：0
|
作者：
Pelekanou, Antonia [1 ]
Anastasopoulos, Markos [2 ]
Tzanakaki, Anna [1 ,2 ]
Simeonidou, Dimitra [2 ]
机构：
[1] Univ Athens, Athens, Greece
[2] Univ Bristol, HPN Grp, Bristol, Avon, England
来源：
22ND INTERNATIONAL CONFERENCE ON OPTICAL NETWORK DESIGN AND MODELING (ONDM 2018) | 2018年
基金：
欧盟地平线“2020”;
关键词：
Machine Learning; Optimization; 5G; ILP; LSTM; MLP; optical transport;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This study proposes a modeling framework for optimal online 5G service provisioning, based on low computational complexity machine learning techniques such as Neural Network (NNs). NNs are trained to take optimal decisions adopting an offline Integer Liner Programming (ILP) model. This framework is used to solve the generic joint Fronthaul (FH) and Backhaul (BH) service provisioning problem over a converged high capacity and flexibility optical transport aiming at minimizing the overall energy consumption of the 5G infrastructure. Our modeling results indicate that the proposed approach adopting NN based real time service provisioning can provide very similar performance to the one derived adopting the high complexity but accurate ILP approach.
引用
收藏
页码：200 / 205
页数：6
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