CCNode Load Balancing for Base Station Traffic Distribution
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Solution Overview
Problem
Existing load balancing methods in communication systems, particularly in scenarios with high user demand like stadiums and concerts, often result in overloading of target cells due to inadequate distribution of traffic, leading to inefficiencies and poor user experience.
Innovation Solution
A centralized control node (CCNode) system that connects multiple base stations through Xt interfaces, enabling real-time load balancing by determining cells with high loads and redistributing users to neighboring cells with lower loads, ensuring balanced traffic distribution and efficient cell switching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If load balancing is implemented by redirecting traffic from multiple cells to the same target cell, then the load is balanced, but the target cell becomes overloaded
Solution Approach 1:
The patent implements differentiated load balancing strategies for different cell types. Macro cells handle high-capacity traffic while small cells (femto cells, pico cells) handle localized traffic. Each cell type has customized admission control parameters and load balancing policies tailored to its specific capacity and coverage characteristics, preventing macro cells from being overloaded by redirecting excessive traffic to small cells.
Solution Approach 2:
The system dynamically adjusts load balancing parameters based on real-time cell load conditions, user equipment (UE) capabilities, and network requirements. The enhanced load balancing algorithm continuously monitors cell status and adapts traffic redirection decisions, migrating UEs from overloaded cells to underloaded cells while considering small cell capacity limits and UE mobility patterns.
2Productivity
If traditional load balancing methods are used, then implementation is simple, but the balancing effect is not ideal
Solution Approach 1:
The patent introduces an Enhanced Load Balancing (ELB) algorithm as an intermediary layer between traditional load balancing mechanisms and the actual traffic redirection process. This ELB algorithm serves as a smart mediator that evaluates multiple factors including cell load, UE capabilities, small cell status, and migration success rates to make optimized balancing decisions, achieving superior load distribution without requiring complete system redesign.
Solution Approach 2:
The load balancing function is segmented into distinct components: traditional load balancing mechanisms handle macro-cell traffic, while the enhanced ELB algorithm specifically manages traffic involving small cells. The system separates admission control, load evaluation, and traffic redirection into independent modules with customized parameters for different cell types, allowing complex balancing logic to be implemented without overwhelming system-wide complexity.
3Reliability
If user migration is performed without considering success rates, then the process is fast, but the reliability of migration is low
Solution Approach 1:
The system performs preliminary evaluation of migration feasibility before executing user equipment migration. The enhanced load balancing algorithm assesses target cell capacity, UE compatibility, and historical migration success rates for specific cell pairs in advance. This preliminary action identifies high-success migration candidates, ensuring that migration attempts are made only to cells with adequate capacity and favorable conditions, thereby improving success rates without significant time penalty.
Solution Approach 2:
The patent implements a feedback mechanism that continuously monitors migration outcomes and uses this information to adjust future migration decisions. The system tracks migration success rates, cell load changes, and UE performance after migration, feeding this data back to the ELB algorithm. This feedback loop enables the system to learn from past migrations and optimize future decisions, improving reliability while maintaining efficiency through data-driven adjustments.
Data Source
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AI summary
Provided are a method for realizing load balancing, a Centralized Control Node (CCNode), a base station and a storage medium. The method is performed by a CCNode connected to multiple base stations. The method may include that: a cell to be balanced is determined; the number of pieces of User Equipment (UEs) to be migrated from the cell to be balanced to at least one available neighboring cell of the cell to be balanced is determined; and a load balancing instruction is sent to a base station to which the cell to be balanced belongs, wherein the load balancing instruction carries the number of UEs to be migrated from the cell to be balanced to the at least one available neighboring cell.