Predictive QoS Analysis for Dynamic Network Resource Allocation
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Solution Overview
Problem
Existing communication networks face challenges in ensuring that experienced QoS meets or exceeds provisioned QoS, particularly in large areas with demanding requirements, such as in vehicular communication, due to deployment costs and resource limitations.
Innovation Solution
Implementing predictive QoS using machine learning (ML) to forecast QoS metrics and dynamically adjust network resources based on these predictions, combined with network resource allocation to ensure QoS satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network improvements are implemented to ensure provisioned QoS satisfaction, then QoS reliability is improved, but deployment costs increase
Solution Approach 1:
The system performs preliminary QoS prediction using machine learning models before services are deployed or scaled. By predicting QoS metrics in advance for different geographical areas and scenarios, the network operator can identify potential QoS issues beforehand and plan resource allocation proactively, avoiding costly reactive network improvements
Solution Approach 2:
The machine learning model continuously learns from network performance data and automatically updates its predictions. The system serves itself by using historical QoS data to improve its own prediction accuracy over time, reducing the need for manual network adjustments and expensive infrastructure upgrades
2Area of stationary object
If network resources are increased to meet demanding QoS requirements over large areas, then QoS coverage is improved, but deployment costs increase
Solution Approach 1:
The system provides customized QoS predictions for different geographical locations by training and deploying machine learning models that learn local network characteristics. Each region can have tailored predictions based on its specific traffic patterns, terrain, and infrastructure, allowing efficient resource allocation without uniform network upgrades across the entire coverage area
Solution Approach 2:
By predicting QoS metrics in advance for different geographical areas, the system identifies which regions require network improvements and which can operate with existing resources. This preliminary analysis enables targeted deployment in high-priority areas rather than blanket upgrades across the entire coverage area
3Measurement precision
If machine learning models are trained with more data, then prediction accuracy is improved, but data availability requirements increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction from available network data before model training. By pre-processing data to extract relevant features and patterns, the system maximizes the information content of limited data, achieving better prediction accuracy without requiring proportionally more raw data
Solution Approach 2:
The machine learning model can adapt its parameters and complexity based on the amount of available training data. When data is limited, the system adjusts model hyperparameters, feature selection, and complexity to achieve optimal performance with the available data, rather than requiring a fixed large dataset
Data Source
AI summary
Aspects of embodiments provide methods and network nodes for Quality of Service (QoS) analysis in a communications network, wherein the communications network provides coverage for a coverage area. The method may include generating a QoS prediction that specifies one or more Key Performance Indicator (KPI) criteria for the communications network to satisfy in relation to one or more devices at a coordinate within the coverage area. The method may further include analysing the QoS prediction and determining a success metric for the communication network satisfying the QoS prediction based on current network resource allocation. If the success metric that the communications network will satisfy the QoS prediction is acceptable the current network resource allocation may be retained. If the success metric that the communications network will satisfy the QoS prediction is not acceptable the current network resource allocation may be altered.