Predictive Carrier Frequency Model for Load Balancing
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Solution Overview
Problem
Current communication networks face inefficiencies in load balancing due to the inability to accurately predict radio conditions on all carrier frequencies, leading to unnecessary resource waste and suboptimal capacity utilization, especially with the introduction of new radio technologies like mmWave frequencies which increase the number of carriers.
Innovation Solution
A method and system for determining a predictive model that uses measurements from a subset of carrier frequencies to estimate conditions on other frequencies, allowing for improved load balancing and resource allocation without the need for individual measurements on each carrier, reducing signaling overhead and battery consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If measurements are performed on all carrier frequencies, then prediction accuracy of radio conditions is improved, but signaling overhead and battery consumption increase
Solution Approach 1:
The patent applies partial action by performing measurements only on a selected subset of carrier frequencies rather than all frequencies. The network node identifies which frequencies require measurement based on current traffic patterns and load conditions, measuring only those necessary for accurate prediction, thereby reducing battery consumption while maintaining sufficient prediction accuracy.
Solution Approach 2:
The patent changes the parameter of measurement frequency selection dynamically. Instead of fixed measurement configurations, the system adapts which carrier frequencies are measured based on changing network conditions, traffic load, and prediction accuracy requirements, optimizing the balance between accuracy and energy consumption.
2Measurement precision
If measurements are performed on all carrier frequencies, then prediction accuracy of radio conditions is improved, but signaling overhead increases
Solution Approach 1:
The system performs measurements only on a subset of carrier frequencies that are most relevant for current network conditions. By selectively measuring only necessary frequencies based on traffic patterns and load balancing needs, the patent reduces signaling overhead while maintaining adequate prediction accuracy for network operations.
Solution Approach 2:
The measurement configuration parameters are dynamically changed based on network state. The network node adjusts which carrier frequencies are included in measurement reports according to current traffic demands and prediction accuracy requirements, reducing unnecessary signaling while maintaining operational accuracy.
3Productivity
If the number of carriers is increased to increase network capacity, then network capacity utilization is improved, but the complexity of load balancing increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between raw measurement data and load balancing decisions. This model automatically processes measurements from multiple carrier frequencies and outputs prediction results, simplifying the load balancing complexity while enabling effective utilization of increased network capacity across many carriers.
Solution Approach 2:
The system dynamically adjusts measurement and reporting parameters based on the number of active carriers and current network conditions. When capacity is increased through additional carriers, the system adapts the measurement subset size and frequency selection criteria, maintaining manageable complexity while utilizing the expanded capacity.
4Speed
If early measurement based setup is implemented for fast CA or DC setup, then setup speed is improved, but resource waste increases due to unqualified load balancing measurements
Solution Approach 1:
The patent applies preliminary action by performing measurements on a selected subset of carrier frequencies before CA or DC setup decisions are made. This early but targeted measurement approach enables fast setup while avoiding waste, as measurements are performed only on frequencies most likely to result in successful load balancing or aggregation configurations.
Solution Approach 2:
The measurement configuration parameters are dynamically adjusted based on current network conditions and UE characteristics. The system changes which carrier frequencies are measured and for how long, optimizing the balance between setup speed and resource consumption for each specific scenario.
Data Source
AI summary
Methods and apparatus in a communications network as described herein are suitable for determining a first model for use in predicting conditions on carrier frequencies in the communications network. In a method, network measurements are obtained (302) of conditions on a plurality of carrier frequencies. A first model is then determined (304) based on the obtained measurements that takes as input the conditions on a first subset of the plurality of carrier frequencies and outputs a prediction of the conditions on a second subset of the plurality of carrier frequencies, based on the conditions of the first subset of carrier frequencies.