ML-Based Secondary Cell Selection for Carrier Aggregation Balancing
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Solution Overview
Problem
Existing carrier aggregation systems fail to optimally distribute user equipment among available cells, leading to overloading and suboptimal user throughput due to uniform cell selection without considering load and radio quality variations.
Innovation Solution
A network node device employs machine learning models, specifically reinforcement learning and recurrent neural networks, to predict and dynamically set secondary cell weight parameters based on downlink throughput and spectral efficiency, enabling optimal secondary cell selection for carrier aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If all UEs are assigned the same set of primary and secondary cells, then cell configuration is simplified, but cells become fully loaded and user throughput deteriorates
Solution Approach 1:
The patent applies local quality by assigning different secondary cell sets to different UEs based on their specific characteristics (mobility, service type, location). Instead of uniform cell configuration, the system customizes cell assignments for each UE group, thereby preventing cell overload and optimizing throughput for each user's specific needs.
Solution Approach 2:
The patent implements dynamics by enabling the network to dynamically adjust secondary cell assignments based on real-time conditions. The system can modify which UEs aggregate which secondary cells according to changing load conditions and radio quality, allowing the system to adapt to varying demands and maintain optimal performance.
2Quantity of substance
If the network adds more cells than UEs can aggregate, then spectrum resources are increased, but load distribution becomes suboptimal without individual cell consideration
Solution Approach 1:
The patent applies parameter changes by using machine learning models to predict and optimize cell weight parameters. The system adjusts secondary cell weight parameters based on predicted load and radio quality conditions, enabling intelligent load distribution across available spectrum resources rather than uniform allocation.
Solution Approach 2:
The patent implements feedback mechanisms where the network monitors actual cell performance and uses this information to refine machine learning predictions. The system continuously learns from observed data to improve its ability to distribute UEs across cells, optimizing load distribution as more cells are added to the network.
3Productivity
If machine learning models are used to predict and optimize secondary cell weights, then downlink throughput and spectral efficiency are improved, but system complexity increases
Solution Approach 1:
The patent applies self-service by implementing self-learning mechanisms where the machine learning models automatically adapt to network conditions without manual reconfiguration. The system uses historical data and real-time observations to autonomously optimize cell weight parameters, reducing the need for complex manual intervention while maintaining high throughput performance.
Data Source
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AI summary
Devices, methods and computer programs for machine learning (ML) -based secondary cell selection for carrier aggregation (CA) are disclosed. At least some example embodiments may allow dynamically predicting a secondary cell weight parameter which may influence a network node decision in selecting secondary cells for user devices.