Uplink Power Control Optimization via Closed-Loop Feedback
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Solution Overview
Problem
Current methods for setting uplink power parameters in LTE networks do not account for interference and user session performance, leading to suboptimal power transmission and poor user experience.
Innovation Solution
A closed-loop automation system that dynamically adjusts uplink power control parameters by observing current performance, predicting improvements, and making adjustments through APIs exposed at the base station, using a trained performance model to identify and quantify issues and optimize power settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If default power levels are broadcast for PUCCH and PUSCH, then device complexity is reduced and ease of operation is improved, but uplink signal quality and SINR deteriorate due to suboptimal power transmission
Solution Approach 1:
The system enables self-service by allowing the network to automatically monitor uplink signal quality metrics (SINR, throughput) and autonomously adjust power parameters without requiring manual administrator intervention. The closed-loop system continuously optimizes power settings based on observed performance, making the system self-correcting and adaptive to changing conditions.
Solution Approach 2:
The invention implements feedback mechanisms where the system continuously monitors uplink signal quality metrics including SINR and throughput, compares actual performance against target thresholds, and uses this feedback to dynamically adjust power parameters. This closed-loop feedback ensures optimal power settings are maintained adaptively without manual intervention.
2Ease of manufacture
If power parameters are set based on estimated path loss, then initial power configuration is simplified, but interference from various sources at the base station is not accounted for leading to poor SINR
Solution Approach 1:
The system dynamically changes power parameters (pZeroNominalPucch, pZeroNominalPusch, alpha) based on monitored performance metrics and calculated interference levels. Rather than using fixed parameters based solely on path loss estimates, the system continuously adjusts these parameters in response to changing interference conditions and signal quality measurements, optimizing performance adaptively.
Solution Approach 2:
The invention implements feedback mechanisms where the system continuously monitors uplink signal quality metrics including SINR and throughput, compares actual performance against target thresholds, and uses this feedback to dynamically adjust power parameters. This closed-loop feedback ensures optimal power settings are maintained adaptively without manual intervention.
3Reliability
If administrators manually detect and adjust power settings, then service awareness and control are improved, but time consumption and operational complexity increase
Solution Approach 1:
The system enables self-service by allowing the network to automatically monitor uplink signal quality metrics (SINR, throughput) and autonomously adjust power parameters without requiring manual administrator intervention. The closed-loop system continuously optimizes power settings based on observed performance, making the system self-correcting and adaptive to changing conditions.
Solution Approach 2:
The invention implements feedback mechanisms where the system continuously monitors uplink signal quality metrics including SINR and throughput, compares actual performance against target thresholds, and uses this feedback to dynamically adjust power parameters. This closed-loop feedback ensures optimal power settings are maintained adaptively without manual intervention.
4Ease of operation
If pZeroNominalPucch is set to -115 dBm with interference power at -110 dBm, then power parameter configuration is straightforward, but uplink SINR deteriorates to only -5 dBm resulting in poor voice quality
Solution Approach 1:
The system dynamically changes power parameters (pZeroNominalPucch, pZeroNominalPusch, alpha) based on monitored performance metrics and calculated interference levels. Rather than using fixed parameters based solely on path loss estimates, the system continuously adjusts these parameters in response to changing interference conditions and signal quality measurements, optimizing performance adaptively.
Solution Approach 2:
The invention implements feedback mechanisms where the system continuously monitors uplink signal quality metrics including SINR and throughput, compares actual performance against target thresholds, and uses this feedback to dynamically adjust power parameters. This closed-loop feedback ensures optimal power settings are maintained adaptively without manual intervention.
Data Source
AI summary
A system can include an evaluation platform that applies models to improve the quality of experience of users impacted by uplink interference at a network cell, such as at a base station. For a user session at a cell, an expected performance with optimized uplink interference can be compared to an actual performance to determine whether the session is impacted. This can include iteratively increasing a hypothetical power parameter and determining uplink interference based on source and neighboring cells. When positively impacted sessions exceed a threshold, the platform can dynamically change the power parameter of the base station to reflect the hypothetical value.


