Cell Antenna Adjustment Using User Distribution Prediction
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Solution Overview
Problem
Existing communication networks struggle to accurately predict future user distribution changes, hindering effective network optimization and user experience improvement.
Innovation Solution
A model training method using deep learning technology to predict future user distribution based on historical data, employing ConvLSTM or GAN-LSTM models, and adjust cell antennas accordingly to adapt to anticipated changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network optimization methods are used, then network parameters can be adjusted based on current user distribution, but the ability to predict future user distribution changes is insufficient
Solution Approach 1:
The patent applies preliminary action by training the prediction model using historical user distribution data from multiple time windows before actual network optimization decisions are made. The model performs preliminary predictions about future user distribution, allowing the network to proactively adjust parameters before user distribution actually changes, thereby improving both prediction accuracy and optimization effectiveness
Solution Approach 2:
The patent implements feedback mechanisms by comparing predicted user distribution with actual observed distribution, calculating prediction errors, and using this feedback to continuously refine and retrain the prediction model. This closed-loop feedback system ensures the model maintains high accuracy while providing reliable basis for network optimization
2Measurement precision
If deep learning models like ConvLSTM or GAN-LSTM are used to predict user distribution, then prediction accuracy improves, but model complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex prediction task into multiple processing stages using different time windows (first time window for initial prediction, second time window for verification). This segmentation allows the system to manage model complexity by processing data in discrete, manageable temporal segments rather than attempting to analyze all historical data simultaneously
Solution Approach 2:
The patent uses partial action by selectively using only the necessary historical time windows required for accurate prediction, rather than processing all available historical data. The model performs prediction using a limited set of relevant features from specific time windows, reducing computational complexity while maintaining prediction accuracy through focused analysis of critical temporal patterns
Data Source
AI summary
The present application provides a model training method and apparatus, a cell antenna adjustment method and apparatus, and an electronic device. The model training method includes: acquiring a user distribution spatio-temporal sequence sample of a target cell; inputting the user distribution spatio-temporal sequence sample to a user distribution prediction model to obtain predicted user spatial distribution data of the target time window corresponding to the target cell; determining training loss of the user distribution prediction model on the basis of the user spatial distribution data of the target time window with which the user distribution spatio-temporal sequence sample is labeled, and the predicted user spatial distribution data of the target time window provided by the user distribution prediction model; and performing parameter adjustment on the user distribution prediction model on the basis of the training loss.