OTN-over-WDM optimization in 5G networks: key challenges and innovation opportunities

被引:7
|
作者
Moubayed, Abdallah [1 ,2 ]
Manias, Dimitrios Michael [1 ]
Javadtalab, Abbas [3 ]
Hemmati, Mahdi [3 ]
You, Yuren [3 ]
Shami, Abdallah [1 ]
机构
[1] Western Univ, London, ON, Canada
[2] Lebanese Amer Univ, Blat Area, Byblos, Lebanon
[3] Huawei Technol Canada Res Ctr, Ottawa, ON, Canada
关键词
OTN-over-WDM optimization; 5G networks; Challenges; Innovation opportunities; PERFORMANCE EVALUATION; MODEL; 5G-CROSSHAUL; ARCHITECTURE;
D O I
10.1007/s11107-022-00991-x
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The continued growth of both mobile broadband and fixed broadband subscriptions as well as the added deployment of Internet of Things devices has led to making 5G networks a reality. More specifically, 5G networks are expected to support a diverse set of new applications/services in addition to existing applications/services from previous generations (2G/3G/4G). The COVID-19 pandemic has further increased the demand for such services which has resulted in a further surge in the Internet usage. Thus, 5G networks are expected to have a highly flexible architecture at all levels including at the radio, core, and transport levels. Optical Transport Networks (OTN) have been proposed as one potential and promising supporting technology for 5G networks at the transport level, particularly for next generation transport networks featuring large-granule broadband service transmissions. This is because it allows for more flexible, efficient, and dynamic networks. However, adopting and deploying OTNs in 5G networks comes with its own set of challenges including control, management, and orchestration of such networks as well as their security. Accordingly, this paper overviews 5G networks along with their requirements and provides a brief summary of OTNs and the corresponding optimization mechanisms. Additionally, this work discusses the challenges facing OTNs and their optimization within the context of 5G. Moreover, it outlines some of the key research areas and opportunities for innovation stemming from the data-driven intelligent networking paradigm using Machine Learning techniques.
引用
收藏
页码:49 / 66
页数:18
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