MDAS Server Handover Optimization in 5G Wireless Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Radio Access Network (RAN) handover mechanisms in 5G wireless networks often result in resource wastage due to inadequate virtual and radio resources at target gNBs, leading to handover rejections and inefficiencies.
Innovation Solution
A method utilizing a Management Data Analytic Service (MDAS) server to periodically collect and analyze resource consumption data from multiple gNBs, generating reports that identify current and future resource availability, and providing corrective actions to optimize resource allocation for handover, thereby ensuring efficient resource utilization and minimizing handover failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current RAN handover mechanisms are used, then handover requests are processed by target gNBs, but resource wastage occurs due to inadequate virtual and radio resources at target gNBs leading to handover rejections
Solution Approach 1:
The MDAS server performs preliminary analysis of resource consumption trends at target gNBs before handover occurs. By predicting future resource availability and identifying optimal target gNBs in advance, the system can proactively prepare for handover, ensuring resources are available when needed and avoiding last-minute rejections.
Solution Approach 2:
The system continuously monitors and analyzes resource consumption data from target gNBs, feeding this information back to the MDAS server. This feedback loop enables the server to update its predictions about resource availability and adjust handover recommendations accordingly, improving the accuracy of resource allocation decisions.
2Adaptability or versatility
If hit-and-trial mechanism is used for handover, then UE tries to connect to different gNBs, but UE resources and wireless network resources are wasted
Solution Approach 1:
The MDAS server acts as an intermediary between the UE and multiple gNBs. Instead of the UE independently trying to connect to different gNBs through hit-and-trial, the MDAS server analyzes resource availability and provides optimized handover recommendations, serving as a mediator that coordinates the handover process more efficiently.
Solution Approach 2:
The MDAS server performs preliminary identification of suitable target gNBs based on predicted resource availability before the handover process begins. This pre-filtering of options reduces the number of trial connections the UE needs to attempt, saving both UE and network resources while maintaining adaptability.
3Reliability
If handover request is rejected due to inadequate resources, then UE must try different gNBs, but this increases handover time and complexity
Solution Approach 1:
The MDAS server performs preliminary analysis of resource consumption patterns and predicts future resource availability at candidate target gNBs before handover is needed. By identifying optimal targets in advance based on predicted availability rather than reacting to current state alone, the system reduces handover delays and rejection cycles.
Solution Approach 2:
The system uses continuous feedback on resource consumption data to dynamically update predictions about target gNB availability. This feedback mechanism allows the MDAS server to provide more accurate and timely handover recommendations, reducing the time spent on rejected requests and multiple trial connections.
Data Source
AI summary
The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. Accordingly, the embodiments herein provide a method for Management data analytic service (MDAS) server (100) assisted handover optimization in a wireless network (1000). The method includes periodically collecting data from a plurality of target gNBs in the wireless network (1000), and generating an analytical report for each target gNB (300) of the plurality of target gNBs based on the collected data. Further, the method includes receiving a request for the analytical report of the at least one target gNB (300) for handover from a source gNB (200), sending the analytical report to the source gNB (200). Further, the includes performing at least one corrective action suggested by the analytical report to optimize at least one target gNB (300) for handover.