Dynamic Wireless Resource Allocation via Base Station Morphology Classification
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Solution Overview
Problem
Wireless networks inefficiently allocate resources between different transmission services due to static and inflexible allocation methods, leading to suboptimal use of resources such as LTE resources, bandwidth, and capacity.
Innovation Solution
Classifying base stations into different morphologies (rural, suburban, urban) based on geographic location, distance, and resource utilization, and using a resource scheduling assistant to generate tailored resource allocation recommendations for each morphology, allowing for dynamic adjustment of resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static resource allocation is used between unicast and eMBMS services, then resource allocation is simple and stable, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation by allowing base stations to adjust resource allocation between unicast and eMBMS services based on real-time service demands and network conditions. The resource allocation is no longer static but adapts continuously to changing traffic patterns, ensuring optimal utilization of available resources while maintaining manageable complexity through automated decision-making algorithms.
Solution Approach 2:
The patent changes the allocation parameters dynamically by adjusting the proportion of resources allocated to different services based on measured service demands. Instead of fixed parameter values, the system modifies allocation parameters in response to varying network conditions, thereby improving resource utilization efficiency without requiring overly complex manual configuration.
2Adaptability or versatility
If uniform resource allocation is applied to all base stations, then configuration and management are simplified, but adaptability to different service demands deteriorates
Solution Approach 1:
The patent applies local quality by allowing each base station to have customized resource allocation configurations tailored to its specific service demands and network conditions. Instead of uniform allocation across all base stations, each base station receives optimized allocation parameters based on local measurements and requirements, thereby improving adaptability while the system manages complexity through automated generation of these localized configurations.
Solution Approach 2:
The patent segments the network into individual base station units, each with its own resource allocation profile. This segmentation allows independent optimization of resource allocation at each base station level based on local conditions, improving overall system adaptability. The complexity is managed by applying the same allocation methodology across segmented units rather than creating entirely unique configurations for each.
3Productivity
If manual resource allocation adjustment is performed, then control precision is maintained, but response time to changing demands increases
Solution Approach 1:
The patent implements automated feedback mechanisms where base stations continuously monitor service demands and resource utilization, then automatically adjust resource allocation in response to measured conditions. This closed-loop feedback system maintains precision in resource allocation decisions while significantly improving response speed to changing service demands, eliminating the delays inherent in manual adjustment processes.
Solution Approach 2:
The patent enables base stations to perform self-service by automatically adjusting their own resource allocation based on locally measured service demands. Each base station independently monitors its own conditions and makes autonomous allocation decisions, thereby achieving both rapid response to changing demands and precise allocation control without requiring manual intervention or external coordination.
Data Source
AI summary
A system may receive base station configuration data associated with base stations, classify the base stations into morphologies based on the base station configuration data, identify resource utilization data associated with the base stations, generate resource allocation recommendations for the base stations based on the resource utilization data and the morphologies, and transmit the resource allocation recommendations for the base stations to permit the stations to allocate resources based on the resource allocation recommendations.