Wireless QoS Prediction via Time-Series Environmental Modeling
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Solution Overview
Problem
Existing methods for predicting the quality of service (QoS) of wireless communication links between mobile transceivers, such as those in vehicular applications, primarily focus on instantaneous predictions rather than tracking the gradual development of the environment, leading to limited performance and reactivity in varying conditions.
Innovation Solution
A method using time-series projection to predict a future environmental model, which is then used to determine the future quality of service by a machine-learning model, allowing for the forecasting of QoS over a timeline by extrapolating environmental data into the future.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If instantaneous QoS prediction methods are used, then the system structure remains simple, but the prediction accuracy and proactivity in varying environmental conditions deteriorates
Solution Approach 1:
The patent applies preliminary action by predicting future QoS conditions before they actually occur. The system uses historical environmental data and machine learning models to forecast future QoS parameters (such as packet error rate, latency, throughput) in advance, enabling proactive rather than reactive communication management. This allows the system to prepare for upcoming quality degradation before it impacts performance.
Solution Approach 2:
The patent implements dynamics by transitioning from static instantaneous predictions to dynamic future predictions that evolve with changing environmental conditions. The system continuously updates its predictions based on historical data and environmental changes, making the QoS prediction adaptive to varying mobility patterns, interference conditions, and network load over time.
2Reliability
If time-series projection is used to predict future environmental models, then the QoS prediction becomes proactive and accurate, but the computational complexity and processing time increases
Solution Approach 1:
The system performs preliminary computational work by pre-processing historical environmental data and training machine learning models in advance. This preliminary action includes collecting and storing past environmental measurements (location, speed, interference levels) and QoS outcomes, then using this pre-trained knowledge to make future predictions without requiring complex real-time computations during actual prediction moments.
Solution Approach 2:
The patent uses copying by creating a virtual replica of the environmental model through time-series projection. Instead of directly computing complex future states, the system copies historical environmental patterns and extrapolates them forward using learned relationships between environmental factors and QoS parameters, simplifying the computation while maintaining prediction accuracy.
3Measurement precision
If the system tracks gradual environmental development over time, then the QoS prediction becomes more accurate in changing conditions, but the data processing requirements and system overhead increases
Solution Approach 1:
The patent applies segmentation by dividing the environmental data into distinct temporal segments or time windows. Instead of processing the entire historical dataset at once, the system segments data into manageable time periods (e.g., recent past, longer-term trends) and processes each segment separately. This allows the system to track gradual environmental development while controlling data processing volume through selective attention to relevant time periods.
Solution Approach 2:
The system implements local quality by focusing data processing on specific locally relevant factors rather than processing all available data uniformly. The machine learning model identifies and weights only the most influential local environmental features (such as immediate nearby vehicles, current interference levels, recent mobility patterns) while filtering out less relevant global data, thereby reducing overall data processing requirements while maintaining prediction precision.
Data Source
AI summary
An apparatus, a method and a computer program for predicting a future quality of service of a wireless communication link based on a predicted future environmental model that is predicted using a time-series projection. The method includes determining environmental models of one or more active transceivers in the environment of the mobile transceiver over points in time, determining a predicted future environmental model of the one or more active transceivers at a point in time of the future using a time-series projection of environmental models, predicting the future quality of service of the wireless communication link for point in time of the future using a machine-learning model. The machine-learning model is trained to provide information on a predicted quality of service for a given environmental model, and the predicted future environmental model is used as input to the machine-learning model.