The Future Roles of Artificial Intelligence in Securing and Optimizing services of 5G over Optical Transport Network

By Ayman Elmaasarawy

The existing challenges in the backhaul of the 4G networks and the Optical Transport Networks (OTN) affect the performance, the flexibility, and the cost of building the next generation of the communication network in Egypt. At the same time, the solutions to these challenges will lead to the evolution of the 5G mobile networks, which are characterized by an increasing number of wireless devices, service complexity, and the high demands to access more mobile services. The thesis discusses the impact of the implementations of the new technologies such software-defined network and machine learning in the core optical network on the flexible functionality of the access networks and how the centralized controller in the heterogeneous networks could improve the capacity and the resilience of the 4G services. There are many challenges of the LTE network in the backhaul section and the core OTN, such as the integration between the different heterogamous optical networks, synchronization of the network, the security of the customer data over the core optical network, and the available capacity of the backhaul LTE network. One of these changes is the security of the mobile customers over the backbone optical network such as OTN. The proposed security model in this thesis is implemented on the basis of protecting the vital client signals only over the optical layers by passing these signals into extra layer called security layer, this done by adding a new card in the Network Element (NE) to perform this job and by using the concept of the software-defined network (SDN) of the centralized controller with the help of the machine learning technology to perform the automatic detection of any intrusions over the optical layers. Another challenge in building the 4G network in Egypt is the high cost of the leased lines over the core optical network, which is used to connect the 4G sites between the different regions in the country. In this thesis, for the first time an Intelligent Universal Platform (IUF) is proposed to manage and optimize the operational tasks in the optical network. Use cases in two situations As-Is and To-Be are studied, the cases are about the energy consumptions, the fault locations, the circuit creations and finally the variation in the signal to noise ratio. The results show that by using the machine learning (ML) in our platform the time of the fault location, the efforts to create one circuit, the number of the complaints, and the response time to the customer complaints are enhanced by significant percentages, this indicates that the machine learning techniques will play a significant role in monitoring, detecting, localizing the faults, and finally optimizing the resources of optical core network, all without human interventions in the near future. There are many other difficulties in the OTN, such the expensive cost of the multilayer services planning, the quality of the services, and the quality of the resilience, all of these difficulties must be recovered first to cope with the changes in the new generations of the access communication networks. The needs to overcome many of these difficulties become vital nowadays, and depend on many factors in the OTN, such the status of the optical cables, the flexibility, the responsive, and the availability of OTN assets to direct customer control. In this thesis for a new proposed model is introduced by reorganizing the OTN resources to fit the needs of the new generations of the communications market, the model consolidates two promising technologies with each other which are the Software-Defined Network (SDN) and the Machine Learning (ML) to overcome the previous challenges and to reconstruct the control of the traditional OTN to be more smart, virtualized and automated. For the first time, the optical cloud concepts are introduced in the OTN by slicing and virtualizing the various domains with its vendors in the heterogeneous optical network.

• Format: Paperback
• Language: English
• ISBN: B0932CX9VF
• ISBN13: 9798741293768
• Release Date: April 2021
• Publisher: Independently Published
• Length: 118 Pages
• Weight: 0.64 lbs.
• Dimensions: 0.3" x 8.5" x 11.0"