Network Resource Allocation via Spatial-Temporal User Distribution
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Solution Overview
Problem
Current network resource management systems struggle to efficiently allocate and configure resources for a plurality of mobile devices at different locations, as they do not account for spatial-temporal user distribution, leading to suboptimal network performance and resource utilization.
Innovation Solution
The implementation of a spatial-temporal user distribution (STUD) method that collects and analyzes geospatial location data over time to create a weighted distribution function, allowing for dynamic network slice configuration and resource allocation based on the aggregated throughput, Quality of Service (QoS) flows, and application characteristics of user equipment (UEs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network resources are allocated uniformly across all locations, then resource allocation simplicity is maintained, but network performance optimization deteriorates due to ignoring spatial-temporal user distribution
Solution Approach 1:
The patent implements dynamic network resource allocation that adapts to changing spatial-temporal user distributions. The system continuously monitors user location data and dynamically adjusts network slice configurations and resource allocations based on real-time distribution patterns, transforming static resource allocation into a dynamic response mechanism that optimizes network performance according to actual user needs.
Solution Approach 2:
The system performs preliminary analysis of user distribution patterns and predicts future resource requirements based on historical and real-time data. By anticipating user movement and aggregation patterns, the network can pre-configure resource allocations and slice settings before user demands actually arise, improving response time and performance while maintaining manageable complexity through proactive rather than reactive allocation.
2Measurement precision
If detailed geospatial location data is collected and analyzed, then resource allocation precision is improved, but system complexity increases
Solution Approach 1:
The patent extracts only the essential spatial-temporal features from comprehensive geospatial location data that are necessary for resource allocation decisions. Rather than processing all available location information, the system identifies and extracts key parameters such as user density, movement patterns, and aggregation points, reducing processing complexity while maintaining sufficient measurement precision for effective resource allocation.
Solution Approach 2:
The system transforms detailed geospatial coordinates into simplified distribution parameters and metrics that capture user spatial-temporal patterns. By changing the parameter representation from raw coordinate data to aggregated distribution characteristics, the system achieves high measurement precision for resource allocation while reducing the complexity of data processing and analysis.
3Productivity
If network resources are dynamically reconfigured based on user distribution, then resource utilization efficiency is improved, but network management complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the network system automatically monitors user distribution, analyzes patterns, and reconfigures resources without manual intervention. The system uses automated algorithms to interpret spatial-temporal data and adjust network slice configurations, transforming resource management from a manual operation into an autonomous self-optimizing process that improves efficiency while simplifying operator workload.
Solution Approach 2:
The system establishes continuous feedback loops between user distribution monitoring and resource allocation decisions. Real-time data on user locations and network performance feeds back into the allocation algorithm, which automatically adjusts resource distribution in response to observed patterns. This feedback mechanism enables dynamic optimization while maintaining ease of operation through automated closed-loop control rather than manual management.
Data Source
AI summary
A method, comprising the steps of collecting, from an access network element, a plurality of time-related geospatial location information associated to a respective one out of a plurality of mobile devices, wherein the plurality of time-related geospatial location information indicates the respective mobile devices' geospatial locations at certain times; developing, based on the plurality of time-related geospatial location information, a time series cumulative distribution function, wherein the time series cumulative distribution function indicates a probability that a geospatial location defined by its coordinate values of a respective one out of the plurality of mobile devices has coordinate values less than or equal to a selected geospatial location's coordinate values; generating configuration information for the access network element, indicative of a time-related allocation of network resources to the plurality of mobile devices based on the time series cumulative distribution function; and providing the configuration information to the access network element.