5G Network Slice Profiles for Consistent Enterprise QoS
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional network slicing mechanisms fail to provide differentiated quality of service for diverse use cases within a single enterprise, leading to inconsistent performance and a lack of comprehensive analytics and assurance systems for 5G networks.
Innovation Solution
A novel framework that automates the generation of network slice profiles based on device experience modes, translating them into service assurance profiles to maintain service level agreements, using AI/ML models to analyze application performance and generate customized slice profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional 5G network slicing mechanisms are used, then network configuration is simplified, but quality of service consistency across diverse applications deteriorates
Solution Approach 1:
The patent changes the parameters of network slicing by introducing AI/ML-based dynamic adjustment of slice profiles. The system continuously learns from network performance data and automatically optimizes quality of service parameters for different slices, enabling consistent QoS across diverse applications without manual reconfiguration of each slice parameter.
Solution Approach 2:
The network slicing system performs self-service through automated slice profile generation and optimization using AI/ML algorithms. The system autonomously monitors network conditions, identifies performance patterns, and adjusts slice configurations without human intervention, maintaining QoS consistency while reducing operational complexity.
2Productivity
If manual slice profile configuration is used, then control precision is high, but deployment time increases
Solution Approach 1:
The patent replaces manual mechanical configuration processes with AI/ML-based automated systems. The machine learning models generate slice profiles algorithmically based on learned patterns from historical network data, eliminating the need for manual parameter tuning while maintaining or improving configuration precision through data-driven optimization.
Solution Approach 2:
The system performs preliminary action by pre-training AI/ML models on historical network performance data before actual slice deployment. This preliminary learning phase enables the system to rapidly generate accurate slice profiles during deployment without time-consuming manual configuration, achieving both speed and precision.
3Adaptability or versatility
If diverse slice profiles are created for different applications, then service customization improves, but system complexity increases
Solution Approach 1:
The patent applies universality by creating a unified AI/ML-based platform that handles multiple slice profile generation and optimization tasks. This single system serves diverse applications (eMBB, mMTC, URLLC) with different requirements, providing service customization capability while managing complexity through a universal automated approach rather than separate manual processes for each service type.
Data Source
AI summary
Disclosed are systems and methods for a network slice profile generation and deployment within 5G network infrastructures and operating environments. The disclosed systems and methods can provide mechanisms for automatically generating slice profiles based on device experience modes, which can be utilized to generate service assurance profiles that adhere/maintain service level agreements. The disclosed systems and methods can automate the generation of a fully deployable network slice profile, which enables functionality for the scaling of network slices per operational environment of such devices (e.g., modifying existing slices and/or selecting new slices). Moreover, the translation of the slice profile to service assurance profiles can provide network stability, which can improve operator reputation in maintaining service level agreements.