Soft TP-UE Association for Dynamic Network Adaptability
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Solution Overview
Problem
Current wireless network technologies, such as CoMP and C-RAN, are inefficient in managing dynamic and inhomogeneous network structures, failing to effectively consider factors like TP-TP sum mutual interference, cell loads, UE collaboration, equipment types, traffic types, UE importance, and historic network performance for efficient TP-UE association.
Innovation Solution
The implementation of a dynamic and adaptive method for forming collaborative groups of TPs and UEs based on soft associations, using a dynamic association map that considers various metrics such as bonding levels, UE weights, and historic network knowledge, allowing for flexible and efficient data pipelining without relying on traditional cell-association concepts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If hard TP-UE association based on highest RSRP is used, then cell attachment is simplified, but network adaptability to dynamic and inhomogeneous structures deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from static hard association to dynamic soft association. The association map continuously updates based on real-time metrics including RSRP, bonding levels, load conditions, and historic performance, allowing the system to adapt to changing network conditions while maintaining operational simplicity through automated dynamic adjustment.
Solution Approach 2:
The patent changes multiple parameters simultaneously to achieve soft association: it incorporates RSRP measurements, bonding levels between TPs, current load conditions, equipment types, traffic types, UE priorities, and historic performance data. This multi-parameter approach enables the system to balance ease of operation with high adaptability to dynamic network structures.
2Device complexity
If traditional CoMP and C-RAN association techniques are used, then implementation complexity is reduced, but management efficiency of dynamic network structures deteriorates
Solution Approach 1:
The patent segments the association management into distinct components: RSRP measurement module, bonding level calculation module, load assessment module, historic performance tracking module, and association map generation module. This segmentation allows each component to be optimized independently while maintaining overall system efficiency in managing dynamic network structures.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring network performance metrics and historic data, then using this information to dynamically update the association map. The system feeds back performance outcomes to adjust future associations, improving management efficiency through data-driven decision-making while keeping implementation complexity manageable through structured feedback loops.
3Adaptability or versatility
If soft TP-UE association with multiple metrics is implemented, then network adaptability improves, but computational complexity increases
Solution Approach 1:
The patent applies partial action by selectively applying different metrics based on network conditions and UE requirements. Not all metrics are computed with equal depth for every association decision - the system adjusts the level of analysis based on current needs, reducing unnecessary computational complexity while maintaining high adaptability where it matters most.
Solution Approach 2:
The patent implements local quality by applying different levels of metric analysis to different TPs and UEs based on their specific characteristics, locations, and requirements. Boundary UEs receive more intensive analysis involving multiple metrics, while interior UEs use simpler association rules, optimizing the balance between adaptability and computational complexity locally across the network.
4Measurement precision
If dynamic association map with historic network knowledge is used, then association accuracy improves, but information processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing historic network performance data in structured formats before association decisions are needed. This advance preparation of historical information allows the system to quickly reference past performance patterns during real-time association, improving accuracy without overwhelming information processing requirements during critical decision moments.
Data Source
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AI summary
Embodiments of the present invention provide a systematic solution to virtual random access (VRA) that dynamically and adaptively forms various metrics. The present invention facilitates dynamic data pipelining between the network-side and the UE-sides without the need to use the same cell- association concept as current wireless networks and provides soft associations between dynamic groups of UEs and TPs based on various UE, TP, and network related metrics. To provide efficient collaborative grouping and association among TPs and UEs, the present invention features a soft TP-UE association map based on several factors, such as individual UE metrics, TP metrics, effects of neighboring or nearby UEs (e.g., interference), and historical network knowledge.