Dynamic IFLB Controller for Wireless Network Load Balancing
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Solution Overview
Problem
Existing inter-frequency load-balancing (IFLB) systems in wireless communication networks face challenges due to static configuration of parameters, which can lead to mismatches with actual cell conditions, resulting in inefficient load balancing and potential handover failures.
Innovation Solution
A controller is introduced to interface with the radio access network (RAN) through defined APIs, enabling dynamic optimization of IFLB parameters using machine learning and statistical analysis. The controller receives data from the RAN, determines optimal adjustments to IFLB settings, and communicates these adjustments back to the RAN.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static configuration parameters are used for IFLB, then device complexity is reduced and ease of operation is improved, but adaptability deteriorates and load balancing performance worsens
Solution Approach 1:
The patent implements dynamic IFLB parameter configuration where the RAN node automatically adjusts load balancing parameters based on real-time cell conditions. The system transitions from static templates to dynamic adaptation by continuously monitoring cell load, user equipment distribution, and neighbor cell conditions, then adjusting parameters like handover thresholds and load balancing factors accordingly.
Solution Approach 2:
The system establishes a feedback loop where the RAN node monitors actual cell conditions and IFLB performance, then uses this information to automatically adjust parameters. The feedback mechanism compares current load conditions against target conditions and modifies parameters to achieve optimal load distribution, enabling continuous improvement without manual intervention.
2Ease of manufacture
If static template parameters are applied to all cells, then device complexity is reduced and ease of manufacture is improved, but manufacturing precision deteriorates and load balancing accuracy worsens
Solution Approach 1:
The patent enables each cell to have customized IFLB parameters tailored to its specific conditions rather than applying uniform templates. The system determines cell-specific parameters based on local factors such as cell load characteristics, user equipment distribution patterns, neighbor cell conditions, and propagation characteristics, ensuring optimal performance for each location.
Solution Approach 2:
The system dynamically changes IFLB parameters based on actual cell conditions and performance requirements. Parameters such as handover thresholds, load balancing factors, and measurement thresholds are adjusted according to real-time conditions including cell load, SINR distribution, and traffic patterns, enabling precise optimization for each cell's specific characteristics.
3Device complexity
If RAN node performs IFLB with limited data storage, then device complexity is reduced, but measurement precision deteriorates and analysis accuracy worsens
Solution Approach 1:
The patent extracts complex data storage and historical analysis functions from the RAN node to external systems such as the operation and maintenance system or cloud platform. The RAN node retains only essential real-time monitoring capabilities, while detailed statistical analysis and long-term data storage are performed externally, reducing RAN complexity while maintaining measurement precision.
Data Source
AI summary
Various communication systems may benefit from adaptive handling of communication loads. For example, certain wireless communication systems may benefit from a method and application programming interface between a radio access network and a controller to optimize inter-frequency load balancing (IFLB). A method can include receiving policy inputs regarding how the IFLB should be operated. The method can also include negotiating a functional split of IFLB-related functions for the purposes of executing inter-frequency load-balancing. The method can further include receiving a set of data/attributes related to facilitate determination of optimal settings for IFLB.