MME Pool Load Distribution via eNodeB Latency and Location
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Solution Overview
Problem
Current mobile communications systems face inefficiencies in load balancing across Mobility Management Entities (MMEs) due to uniform traffic distribution methods, which do not account for varying latency and geographical differences between eNodeBs and MMEs, leading to suboptimal performance and increased call failures in large geographical areas.
Innovation Solution
Implementing eNodeB-specific relative capacity assignments based on latency and location, allowing for non-uniform distribution of traffic within an MME pool, where eNodeBs closer to MMEs receive higher relative capacities, and enabling dynamic adjustment of capacities through configuration updates to optimize load balancing and offloading processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform traffic distribution is used across all MMEs, then system simplicity is maintained, but network performance deteriorates due to ignoring latency and geographical differences
Solution Approach 1:
The patent applies local quality by assigning different relative capacity values to different eNodeBs based on their specific characteristics (latency, location). Instead of uniform treatment, each eNodeB receives a customized relative capacity value that reflects its local network conditions, thereby improving overall network performance while maintaining a relatively simple load balancing mechanism.
2Reliability
If eNodeB-specific relative capacity assignment is implemented, then network performance improves through granular load management, but system complexity increases
Solution Approach 1:
The patent implements parameter changes by modifying the relative capacity parameter for each eNodeB based on measured latency and location parameters. The system dynamically adjusts these capacity values to optimize traffic distribution, achieving improved network performance through parameter optimization rather than through complex structural changes.
3Ease of manufacture
If traffic is balanced solely based on relative traffic among MMEs, then implementation simplicity is maintained, but call failures increase due to suboptimal load distribution
Solution Approach 1:
The patent applies preliminary action by pre-calculating and assigning relative capacity values to eNodeBs based on their latency and location characteristics before traffic arrives. This preliminary configuration enables the system to automatically make optimal routing decisions without complex real-time calculations, thus maintaining implementation simplicity while reducing call failures.
4Reliability
If granular load management is implemented, then user experience improves, but computational requirements and system complexity increase
Solution Approach 1:
The patent applies segmentation by dividing the network into eNodeB-specific segments, each with its own relative capacity value. This segmentation allows for granular load management where each eNodeB can be independently optimized based on its specific performance characteristics, improving user experience while keeping the overall system manageable through modular configuration.
Data Source
AI summary
Systems and methods are disclosed for a method of assigning resources to eNodeBs in a mobility management entity (MME) pool, including determining a first relative capacity for a first eNodeB based on at least one of latency between the first eNodeB and a first MME and the location of the first eNodeB. A second relative capacity may be determined for a second eNodeB based on at least one of latency between the second eNodeB and the first MME and the location of the second eNodeB. The first and second relative capacities may be indicative of relative capacity values greater than zero. The relative capacities may be provided to the first and second eNodeBs, and a portion of traffic sent from the first and second eNodeBs to the first MME may be based on the first and second relative capacities, respectively.