5G Antenna Resource Allocation for Predicted Idle UE Activation
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Solution Overview
Problem
Existing wireless networks face challenges in efficiently transitioning idle user equipment to active mode due to limited radio resources and the difficulty in predicting and preemptively allocating resources for seamless connectivity, especially with high-speed connections and mmWave bands.
Innovation Solution
A system and method that predicts the transition of idle user equipment to active mode by collecting signal propagation information, modifying messaging protocols, and preemptively allocating antenna resources to ensure timely and efficient network coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If radio resources are allocated to idle user equipment to facilitate transition to active mode, then network connectivity and transition speed are improved, but radio resource availability for other users deteriorates
Solution Approach 1:
The system performs preliminary actions by predicting which idle user equipment will transition to active mode before the transition occurs. Radio resources are pre-allocated to these predicted active UEs during their idle period, so when they transition, the resources are already ready and immediately available, eliminating wait time and improving transition speed without requiring additional resources during peak usage.
Solution Approach 2:
The radio resource allocation is made dynamic and adaptive based on real-time predictions of user equipment behavior. The system continuously monitors idle UEs, predicts their transition likelihood, and dynamically adjusts resource allocation accordingly. This allows the network to optimize resource distribution between idle and active users, improving overall system efficiency and transition performance without permanently dedicating resources.
2Reliability
If radio resources are preemptively allocated to idle user equipment, then attach failures and delays are reduced, but overall network resource efficiency deteriorates
Solution Approach 1:
The system implements feedback mechanisms to continuously monitor the accuracy of transition predictions and the utilization of pre-allocated resources. Based on this feedback, the prediction algorithms are refined and resource allocation strategies are adjusted. This ensures that resources are pre-allocated only to UEs with high probability of transition, minimizing waste and maintaining high network resource efficiency while still reducing attach failures significantly.
Solution Approach 2:
The system changes key parameters such as prediction thresholds, resource allocation ratios, and time windows based on network conditions and observed patterns. By dynamically adjusting these parameters, the system optimizes the balance between reducing attach failures and maintaining resource efficiency, adapting to different traffic patterns and network states without permanent resource commitments.
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 after passage of a time duration, starting from the predicting, that is lower than a time threshold. The method can further include identifying base station equipment that is able to provide coverage to the user equipment during the passage of the time duration before the user equipment transitions to the active mode. Further, the method can include prioritizing allocation of, within the group of user equipment, an antenna resource of the base station equipment to provide the coverage to facilitate an active mode connection by the user equipment to the base station equipment.