RAN Software Upgrade Timing Using Traffic-Aware Analytics
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Solution Overview
Problem
Existing 5G network software upgrades require manual intervention and can disrupt network availability due to the need for minimal traffic conditions, straining telecom operators and causing service disruptions.
Innovation Solution
Implement a machine learning model integrated with a cloud management system to predict an optimal timestamp for software upgrades of virtual network elements based on traffic conditions, generating a bearer information analytical report to facilitate automatic upgrades with minimal impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used for software upgrades, then upgrade timing can be controlled, but operational complexity and costs increase
Solution Approach 1:
The system automatically determines optimal upgrade timing by analyzing traffic patterns and performance metrics without human intervention. The network element itself generates upgrade requests based on predefined criteria, eliminating the need for manual monitoring and decision-making while maintaining service continuity.
Solution Approach 2:
The system continuously monitors traffic conditions, service quality, and network performance, using this feedback to automatically determine when upgrade conditions are met. This closed-loop approach replaces manual intervention with automated decision-making based on real-time network state.
2Reliability
If software upgrades are performed during high-traffic periods, then network availability is maintained, but service quality deteriorates
Solution Approach 1:
The system dynamically adjusts upgrade scheduling based on real-time traffic conditions and service quality metrics. Instead of fixed scheduling, the upgrade timing adapts to changing network conditions, performing upgrades when traffic patterns indicate minimal impact on service quality while maintaining overall network availability.
Solution Approach 2:
The system changes the timing parameter of software upgrades based on analyzed traffic patterns and service quality metrics. By adjusting the temporal parameter of when upgrades occur according to measured network conditions, the system avoids high-traffic periods while maintaining availability during necessary maintenance windows.
3Productivity
If automatic upgrade scheduling is implemented, then operational costs are reduced, but system complexity increases
Solution Approach 1:
The automatic upgrade system is segmented into distinct functional modules: traffic analysis component, upgrade condition evaluation component, and execution component. Each module performs a specific function, making the overall complex system manageable through modular design while maintaining operational efficiency.
4Reliability
If frequent software upgrades are performed, then service quality is improved, but network stability decreases
Solution Approach 1:
The system performs software upgrades selectively based on analyzed conditions rather than on a fixed schedule. By applying upgrades only when traffic patterns and service quality metrics indicate appropriate timing, the system avoids unnecessary disruptions while ensuring upgrades occur when they will most benefit service quality.
Data Source
AI summary
A pre-5th-Generation (5G) or 5G communication system for supporting higher data rates Beyond 4th-Generation (4G) communication system such as Long Term Evolution (LTE). Method and/or electronic device for managing a software upgrade of a network element of a radio access network (RAN) by a management data analytics service (MDAS) producer is provided. The method comprises: receiving a request related to an optimal time for the software upgrade of the network element in the RAN, from a MDAS consumer; identifying information related to a dedicated radio bearer (DRB); identifying information related to the optimal time for the software upgrade of the network element based on the information related to the DBR; and transmitting a report including the information related to the optimal time for the software upgrade of the network element.