Distributed Cell Load Redistribution via Iterative Offset Adjustment
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Solution Overview
Problem
Current load balancing mechanisms in wireless communication networks, particularly in heterogeneous LTE networks, face challenges such as manual configuration requirements, centralized and time-consuming optimization processes, and inability to adapt dynamically to varying load conditions and user mobility.
Innovation Solution
A distributed and localized method that dynamically determines target load values for each cell based on current and neighbor cell loads, using a mechanism that iteratively adjusts range expansion offsets to achieve load redistribution, thereby minimizing manual reconfigurations and addressing scalability issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual configuration of range expansion offset parameter is used, then load balancing can be achieved in static scenarios, but the system cannot adapt dynamically to varying load conditions and UE mobility
Solution Approach 1:
The system performs self-optimization by automatically determining target load values and adjusting range expansion offsets without manual intervention. Each eNodeB monitors its own load and neighbor cell loads, then autonomously calculates and applies offset adjustments to achieve load balancing, eliminating the need for manual configuration while adapting to dynamic conditions.
Solution Approach 2:
The patent implements dynamic load balancing by continuously monitoring load conditions and adjusting range expansion offsets in real-time. The system transitions from static manual configuration to dynamic automated adjustment, allowing the network to adapt to varying load conditions and UE mobility patterns through iterative optimization processes.
2Reliability
If centralized optimization mechanism is used, then load balancing can be coordinated across the network, but the process becomes time-consuming and scales poorly
Solution Approach 1:
The patent divides the centralized optimization process into distributed segments at each eNodeB. Each node independently monitors local and neighbor cell loads, calculates its own target load values, and applies adjustments autonomously. This segmentation eliminates the time-consuming centralized coordination while maintaining effective load balancing through local decision-making.
Solution Approach 2:
Each eNodeB performs self-optimization by independently determining target load values based on local conditions and automatically adjusting range expansion offsets. This self-service approach replaces the slow centralized optimization process, enabling rapid adaptation to changing network conditions without waiting for central coordination.
3Ease of operation
If traditional SINR-based UE to cell allocation is used, then simple allocation rules can be applied, but low power nodes cannot effectively off-load high power nodes due to insufficient transmission power
Solution Approach 1:
The patent changes the parameter used for cell selection from traditional SINR-based metrics to a modified metric that incorporates range expansion offsets. By adjusting this parameter dynamically based on load conditions, the system enables low power nodes to effectively compete for UE allocations despite having lower transmission power, thereby achieving successful off-loading.
Solution Approach 2:
The range expansion offset acts as an intermediary parameter that mediates between the transmission power limitation of low power nodes and the need for effective off-loading. This offset parameter adjusts the perceived signal strength and cell selection preferences, allowing low power nodes to compensate for their power limitations and successfully attract UEs from high power nodes.
Data Source
AI summary
The present invention relates to a method and system for enabling a redistribution of load between cells. The method comprises obtaining a current load value for a first cell, and obtaining a target load value for a neighbor cell. The method also comprises determining the target load value for the first cell as a function of the current load value and the target load value for the neighbor cell, obtaining an update of the target load value for the neighbor cell, and iterating the determining and the obtaining. The method further comprises taking action for a redistribution of load between the first cell and the neighbor cell towards a load distribution represented by the resulting load values.