Base Station Predictive Bearer Loss Management
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Solution Overview
Problem
In cellular wireless networks, the air interface often becomes overloaded, leading to insufficient quality of service for UEs, with existing solutions dropping less important bearers to free capacity, which can cause user experience issues and delay in addressing the problem.
Innovation Solution
A base station predicts impending high air interface load and proactively manages UE service by identifying which bearers would be dropped and the resulting service degradation, taking actions such as handover or alerting UEs before the load threshold is reached.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the base station drops less important bearers to free up air interface capacity, then the air interface load is reduced and service level requirements can be met, but the user experience deteriorates and service continuity is disrupted
Solution Approach 1:
The base station performs preliminary actions by predicting future air interface load conditions before the threshold is reached. When high load is predicted, the system proactively initiates bearer re-establishment procedures, handovers, or UE alerts before actual bearer loss occurs, preventing service degradation while maintaining user experience.
2Reliability
If the base station reacts to high air interface load after it occurs, then the service level requirements can be restored, but there is a delay in addressing the problem causing unacceptable user experience
Solution Approach 1:
The base station uses prediction mechanisms to identify future high load conditions before they materialize. This allows the system to take corrective actions in advance, eliminating the time delay associated with reactive approaches and ensuring continuous service quality without experiencing unacceptable user experience degradation.
3Object-affected harmful factors
If the base station proactively manages UE service by predicting bearer loss, then user experience is maintained, but the system complexity increases due to prediction and evaluation mechanisms
Solution Approach 1:
The base station implements feedback mechanisms by continuously monitoring air interface load conditions and using this information to predict future high load states. The system evaluates predicted bearer loss impacts and adjusts its behavior accordingly, maintaining user experience through informed decision-making while managing complexity through structured feedback loops.
4Object-affected harmful factors
If the air interface capacity is increased to handle all bearers during high load, then all services can be maintained, but the network infrastructure cost and resource requirements increase
Solution Approach 1:
Instead of increasing air interface capacity reactively, the system takes preliminary actions by predicting which bearers are at risk of loss and proactively re-establishing them or initiating handovers before the load threshold is reached. This approach maintains service continuity for all bearers without requiring additional air interface capacity, avoiding increased network infrastructure costs.
Data Source
AI summary
A method and system for improved management of UE service. A base station will predict that its air interface will become threshold highly loaded. And, in response to that prediction but before the predicted threshold high load occurs, the base station will proactively manage service of UEs based on a prediction of how much each UE would suffer from bearer loss if and when the predicted threshold high load occurs. For instance, the base station may predict for each UE a level of service degradation based on service value of each bearer, if any, that the UE would lose if the threshold high load occurs. And the base station may take proactive action, such as triggering a handover and/or providing an alert message, with respect to each UE whose predicted level of service degradation is threshold high.