Communication Prediction Model Segmentation for Frequency Conditions
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Solution Overview
Problem
Current wireless communication systems face challenges in predicting communication quality due to varying frequency conditions, as they require extensive data for all combinations of frequency channels and bandwidths, leading to increased load and inefficiency.
Innovation Solution
A communication apparatus that learns and models relationships between environment information, such as position, orientation, and sensor data, to predict communication quality by selecting and processing appropriate models based on actual frequency channel conditions, enabling accurate prediction of communication quality under various frequency combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If learning is performed for all combinations of frequency channels and bandwidths, then communication quality prediction accuracy is improved, but system load and data requirements increase
Solution Approach 1:
The patent segments the frequency channel conditions into multiple groups, where each group shares a common communication prediction model. Instead of creating separate models for every possible frequency channel combination, the system divides the frequency spectrum into groups and generates one prediction model per group. This segmentation reduces the total number of models needed while maintaining prediction accuracy for specific frequency conditions.
Solution Approach 2:
The patent creates prediction models that can serve multiple frequency channel conditions within a group. A single communication prediction model generated for a group of frequency channels can be universally applied to predict communication quality across all channels in that group, eliminating the need for separate dedicated models for each individual frequency channel.
2Measurement precision
If separate prediction models are generated for each frequency channel condition, then prediction accuracy for specific conditions is improved, but the number of models and data requirements increase
Solution Approach 1:
Frequency channel conditions are segmented into groups based on shared characteristics. Each group contains multiple frequency channel conditions that can be effectively modeled by a single prediction model. This segmentation reduces the total number of prediction models from one per frequency channel to one per group, significantly decreasing the quantity of models while preserving prediction accuracy.
Solution Approach 2:
Multiple frequency channel conditions that share similar propagation characteristics are merged into the same group and represented by a single communication prediction model. This merging approach combines the requirements of multiple individual models into one unified model, reducing the overall number of models needed in the system.
3Adaptability or versatility
If comprehensive data collection for all frequency conditions is performed, then model generality is improved, but time and resource consumption increase
Solution Approach 1:
The patent segments frequency channel conditions into groups, allowing data collection and model generation to be performed separately for each group rather than requiring comprehensive data for all possible frequency conditions simultaneously. This segmented approach reduces the time and resources needed for data collection while maintaining the ability to generate accurate prediction models for each group.
Solution Approach 2:
The system performs preliminary data collection and model generation for each frequency channel group separately, rather than waiting to collect comprehensive data for all frequency conditions. This preliminary action for each group enables earlier deployment of prediction models while reducing the overall time and resource investment required.
Data Source
AI summary
An object of the present disclosure is to enable prediction of communication quality in accordance with wireless communication under frequency conditions as combinations of frequencies and frequency bandwidths.The present disclosure provides a system including: an environment information generation unit configured to generate environment information of a terminal that performs wireless communication; a communication prediction model storage unit configured to store a plurality of communication prediction models obtained by learning relationships between the environment information and communication quality of wireless communication under frequency channel conditions; a communication prediction model generation unit configured to select one or more communication prediction models from among the plurality of communication prediction models and use the selected communication prediction models to generate a communication prediction model; and a communication prediction unit configured to input the environment information generated by the environment information generation unit to the communication prediction model generated by the communication prediction model generation unit and predict current or future communication quality of the terminal.