Base Station Resource Throttling for UE Priority Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Wireless telecommunications networks experience performance degradation and service interruptions during peak hours due to overload, as existing systems equally distribute limited resources among user equipments (UEs), failing to meet the increased service demands.
Innovation Solution
Implementing a reduced priority zone classification system using machine learning to predict UEs at risk of signal loss, allowing network nodes to throttle resources to these UEs while prioritizing those with higher service priority and critical traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If resources are equally distributed among all UEs during high utilization, then system simplicity is maintained, but service quality and user satisfaction deteriorate
Solution Approach 1:
The patent applies local quality by differentiating resource allocation based on UE-specific characteristics. UEs are classified into different priority groups (e.g., critical traffic, best-effort traffic) and received signal strength categories, allowing the system to allocate resources differently to different UEs rather than using uniform distribution. This resolves the contradiction by maintaining system simplicity through automated classification while improving service quality through targeted resource allocation to UEs that can effectively utilize additional resources.
2Ease of operation
If remaining resources are split equally among UEs during overload, then fairness is maintained, but service demand fulfillment deteriorates
Solution Approach 1:
The patent changes the allocation parameters from equal distribution to differentiated distribution based on multiple factors including UE priority level, received signal strength, and traffic type. The system adjusts resource allocation parameters dynamically, allocating more resources to UEs with critical traffic or strong signal conditions, thereby improving service demand fulfillment while maintaining operational simplicity through automated parameter adjustment.
3Reliability
If network resources are allocated to UEs at high utilization, then user service is improved, but network overload and performance degradation worsen
Solution Approach 1:
The patent applies partial action by allocating resources selectively to specific UEs rather than attempting to serve all UEs equally during overload conditions. The system identifies and prioritizes UEs that can benefit most from additional resources (e.g., those with critical traffic or strong signal conditions) while intentionally limiting or excluding resource allocation to UEs that would not effectively utilize additional resources, thereby improving user service for prioritized UEs while maintaining network efficiency.
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
A method performed by a network node (10) for a wireless telecommunications network performs operations including providing (1702) a resource allocation model that corresponds to a base station and that provides a recommendation regarding resource allocation for a user equipment (UE) that is in an operating zone of the base station during a limited resource condition of the base station. Operations may include identifying (1704), based on the resource allocation model, a reduced priority zone in the operating zone of the base station that corresponds to the UE having a high risk of reduced service from the base station relative to other UEs in the operating range of the base station.