5G MRO Architecture for Handover Optimization
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Solution Overview
Problem
The increasing complexity and demand on 5G networks due to diverse communication devices and data bandwidth require improved mobility management to enhance handover performance and reduce radio link failures, which existing technologies have not adequately addressed.
Innovation Solution
The implementation of Mobility Robustness Optimization (MRO) architecture in 5G networks, which dynamically adjusts handover parameters based on performance measurements and events, using self-organizing network (SON) algorithms to optimize handover processes and reduce undesired handovers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing handover management technologies are used in 5G networks, then network basic functionality is maintained, but handover performance is insufficient and radio link failures increase due to network complexity and diverse devices
Solution Approach 1:
The system implements self-service through autonomous MRO functions that automatically detect handover performance issues, analyze failure patterns, and adjust handover parameters without manual intervention. The network system monitors its own handover events, identifies problematic scenarios, and self-optimizes parameters like handover thresholds and timing to improve reliability while managing complexity internally
Solution Approach 2:
The patent implements feedback mechanisms where handover performance measurements and radio link failure data are continuously collected, analyzed, and used to dynamically adjust handover parameters. The system monitors handover events, compares actual performance against targets, and feeds this information back to optimize parameters such as handover triggering thresholds and timing, creating a closed-loop control system that improves reliability adaptively
2Productivity
If handover parameters are manually configured, then initial network operation is possible, but handover performance cannot be dynamically optimized for changing network conditions
Solution Approach 1:
The system transitions from static manual configuration to dynamic automatic optimization. MRO functions continuously monitor network conditions, device types, and traffic patterns, then dynamically adjust handover parameters in real-time. This allows the network to adapt handover behavior to changing conditions such as different device capabilities, traffic loads, and radio environments, thereby increasing both productivity and adaptability
Solution Approach 2:
The patent implements parameter changes by automatically modifying handover-related parameters such as thresholds, timing values, and conditioning criteria based on observed performance. The system adjusts these parameters dynamically to optimize handover performance for different scenarios, enabling the network to handle diverse devices and traffic patterns effectively while increasing overall capacity
3Reliability
If traditional MRO approaches are applied without 5G-specific optimizations, then general mobility management works, but handover performance is insufficient for diverse 5G devices and applications
Solution Approach 1:
The system applies local quality by implementing device-specific and scenario-specific handover optimizations. Different handover parameters and criteria are applied based on device type, capability, and operational context. This allows the network to tailor handover behavior to specific device requirements and local conditions, improving radio link reliability for each device category while managing the complexity of device diversity through localized optimization strategies
Data Source
AI summary
A method and system to provide Mobility Robustness Optimization (MRO) in a NR network are described. The MRO parameters, use case, management services and information definition and procedures are described. A distributed self-organized network (D-SON) management function requests a producer of provisioning management service (MnS) to set targets, handover parameter ranges, and control information for an MRO function and then enables the MRO function for a non-enabled NR cell. The MRO function receives and analyses information from UEs to determine actions to optimize MRO performance. The D-SON management function collects and analyses MRO related performance measurements to evaluate the MRO performance, and updates the targets, handover parameter ranges, and/or control information when the MRO performance does not meet the targets.