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Updated: Jul 9, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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An E2E Network Slicing Framework for Slice Creation and Deployment Using Machine Learning.

Sujitha Venkatapathy¹, Thiruvenkadam Srinivasan², Han-Gue Jo³

¹TIFAC-CORE in Cyber Security, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, Tamil Nadu, India.

Sensors (Basel, Switzerland)

|December 9, 2023

Summary

This summary is machine-generated.

This study uses machine learning to create network slices for 5G, optimizing resource allocation for services like mMTC, eMBB, and uRLLC. The approach enhances user access and resource efficiency while reducing bandwidth use.

Keywords:

5G network machine learning network slicing virtual network embedding virtual network function

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Area of Science:

Telecommunications Engineering
Computer Science
Network Architecture

Background:

5G networks require flexible and dynamic features, achievable through network slicing.
Network Function Virtualization (NFV) and Software-Defined Networking (SDN) are key enablers for network slicing.
Network slicing allows for customized, end-to-end isolated services based on specific requirements.

Purpose of the Study:

To develop a machine learning-based method for constructing network slices.
To efficiently allocate resources to these newly created network slices using dynamic programming.
To optimize key performance indicators (KPIs) such as user access rate and resource efficiency.

Main Methods:

A substrate network was constructed, considering KPIs like CPU capacity, bandwidth, delay, link capacity, and security.
Network slices were generated using a Multi-Layer Perceptron (MLP) with the ADAM optimization algorithm.
Resource allocation was performed using Dijkstra's algorithm to find the shortest path, maximizing user access and resource efficiency.

Main Results:

The proposed model effectively categorizes network slices for different services (mMTC, eMBB, uRLLC).
Optimum network slices were allocated to requested services.
The model demonstrated high resource efficiency and reduced total bandwidth utilization.

Conclusions:

The developed approach successfully constructs and allocates resources for network slices in 5G networks.
The integration of machine learning and dynamic programming leads to efficient resource management.
This method offers a promising solution for enhancing 5G network performance and customization.