Wireless Load Balancing via Predictive KPI Analysis
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Solution Overview
Problem
Current wireless communication systems face challenges in accurately predicting load imbalances and efficiently performing load balancing, particularly in 5G networks, which can lead to suboptimal service quality and increased computational resource usage.
Innovation Solution
A method and apparatus that utilize a server to receive and analyze key performance index (KPI), cell coverage, and service quality information to predict load imbalances, determining a load balance parameter rule set, and transmit it to base stations, enabling proactive measures and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional load balancing methods are used in 5G networks, then service quality can be maintained, but computational resource usage increases and prediction accuracy decreases
Solution Approach 1:
The system performs preliminary prediction of load imbalance using a prediction model that analyzes historical KPI data, cell coverage information, and service quality metrics. By predicting future load conditions in advance, the system can prepare load balancing parameter rule sets before actual load imbalance occurs, reducing the need for intensive real-time computational analysis and thereby lowering computational resource usage while maintaining prediction accuracy.
Solution Approach 2:
The load balancing system operates autonomously by automatically collecting KPI data, predicting load conditions, generating parameter rule sets, and applying load balancing decisions without requiring extensive manual intervention or complex real-time computational resources. The system serves itself by using its own historical data and prediction capabilities to make load balancing decisions, reducing external computational dependencies.
2Reliability
If proactive load balancing is implemented, then service quality is optimized, but system complexity increases
Solution Approach 1:
The system segments the load balancing function into distinct modular components: a prediction model that analyzes historical data, a parameter rule set generator that creates loading rules, and a load balancing execution module that applies rules. This segmentation allows each component to perform its specific function independently, simplifying the overall system architecture while enabling proactive load balancing that optimizes service quality through coordinated operation of these modular elements.
3Area of stationary object
If more base stations are supported, then network coverage is expanded, but resource allocation efficiency decreases
Solution Approach 1:
The system dynamically changes load balancing parameters based on predicted load conditions and actual monitoring results. By adjusting parameters such as load thresholds, handover conditions, and resource allocation ratios in response to predicted and actual load states, the system can efficiently manage resources across a larger number of base stations, thereby expanding network coverage while maintaining resource allocation efficiency through adaptive parameter optimization.
Data Source
AI summary
The present disclosure relates to a 5th generation (5G) or pre-5G communication system for supporting a higher data transmission rate after a 4th generation (4G) communication system such as long-term evolution (LTE). According to various embodiments of the present disclosure, a method performed by a server in a wireless communication system may include receiving key performance index (KPI) information, cell coverage information and service quality information of a base station of a sector managed from a KPI data base (DB), a cell coverage DB, and a service quality DB, generating a predicted KPI, predicted cell coverage and predicted service quality information of the base station of the sector based on the KPI information, the cell coverage information and the service quality information, predicting whether a load imbalance occurs including the base station of the sector based on the predicted KPI, the predicted cell coverage and the predicted service quality information, determining a load balance (LB) parameter rule set including one or more predicted LB parameters based on predicting whether the load imbalance occurs, and transmitting the LB parameter rule set to the base station of the sector is provided.