5G Cell Resource Allocation for Predicted Idle UE Activation
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Solution Overview
Problem
Existing wireless communication networks face challenges in efficiently allocating resources to idle user equipment (UE) transitioning to active mode, leading to attach failures and delays due to misallocation and lack of beam-steering, especially with mmWave frequencies experiencing higher path loss and interference.
Innovation Solution
A system that predicts the transition of idle UE to active mode by collecting signal propagation information and preemptively allocating radio resources, including creating and adjusting energy beams to cover idle UEs, using a controller equipment to prioritize and balance resources among base stations based on predicted load and interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network resources are allocated based on current active load only, then resource allocation is simple, but idle UE transitioning to active mode experience attach failures and delays
Solution Approach 1:
The system performs preliminary actions by predicting which idle UEs will transition to active mode within a specific time window and preemptively allocating resources for these UEs before the actual transition occurs. This involves collecting signal propagation information from idle UEs, predicting their likelihood of transitioning to active mode, and reserving radio resources in advance, thereby eliminating attach failures and delays while maintaining manageable complexity through automated prediction algorithms
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting signal propagation information from idle UEs and using this feedback to update predictions of which UEs will transition to active mode. This feedback loop allows the system to dynamically adjust resource allocation predictions and improve accuracy over time, resolving the contradiction between reliability and complexity through data-driven optimization
2Speed
If mmWave frequencies are used to provide faster broadband connections, then connection speed increases, but path loss and interference increase
Solution Approach 1:
The system applies local quality by creating targeted energy beams directed at specific idle UEs based on their signal propagation characteristics and predicted transition likelihood. Instead of uniform coverage, the system concentrates radio resources in specific directions and locations where UEs are most likely to transition to active mode, thereby maintaining high-speed mmWave connections while compensating for path loss and interference through spatially selective resource allocation
Solution Approach 2:
The system performs preliminary beam formation and resource allocation based on collected signal propagation information from idle UEs before they transition to active mode. By pre-establishing optimal beam directions and resource allocations based on predicted transition patterns, the system ensures that when UEs do transition, they immediately receive high-quality mmWave connections despite path loss and interference conditions
3Reliability
If network resources are allocated to all idle UEs, then attach success improves, but network overhead and resource waste increase
Solution Approach 1:
The system applies partial action by allocating resources only to a subset of idle UEs that are predicted to transition to active mode within a specific time window, rather than allocating resources to all idle UEs. This selective approach uses prediction algorithms to identify the most likely candidates for transition and allocates resources proportionally, thereby maintaining high attach success rates while significantly reducing network overhead and energy waste compared to universal resource allocation
Solution Approach 2:
The system changes parameters by introducing a time window threshold and prediction probability thresholds to determine which idle UEs receive resource allocation. By dynamically adjusting these parameters based on network conditions and transition patterns, the system optimizes the balance between attach success rate and network overhead, allocating resources only when the predicted benefit exceeds the overhead cost
Data Source
AI summary
The technologies described herein are generally directed to providing radio resources to facilitate a predicted transition to active mode by idle user equipment in a fifth generation (5G) network or other next generation networks. An example method can include predicting that a user equipment of a group of user equipment in an idle mode will transition to an active mode during a time duration. The method can further include, identifying base station equipment that are able to provide coverage to the group of user equipment during the time duration. Further, the method can include, based on predicting the user equipment will transition to active mode, prioritizing allocation among the base station equipment, of resources to provide coverage to facilitate an active mode connection by the user equipment to the base station equipment.