Wireless Communication Model Training via Low-Dimensional Data Representation
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Solution Overview
Problem
Current wireless communication systems face challenges in achieving timely model training due to the large size and complexity of models, which require significant computing resources and result in long training times, especially in online training scenarios.
Innovation Solution
The proposed solution involves generating a low-dimensional representation data set from a high-dimensional data set and using this representation for training a wireless communication model. This approach reduces the number of parameters and the size of the model, thereby accelerating the training process and improving timeliness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a model is trained by using a data set in wireless communication systems, then communication performance is improved, but training timeliness deteriorates due to large model size and complexity
Solution Approach 1:
The patent extracts and removes redundant features and parameters from the original data set, keeping only the most essential information needed for model training. This extraction process reduces the data dimensionality while preserving the core communication performance characteristics, enabling faster training without sacrificing reliability
Solution Approach 2:
The patent creates a simplified copy or representation of the original high-dimensional data set by generating a low-dimensional representation that captures the essential patterns. This copied representation is then used for training, reducing computational burden while maintaining the ability to improve communication performance
2Reliability
If the model size and complexity are increased to improve wireless communication performance, then model accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies feature extraction techniques to identify and remove redundant model parameters and complexity, retaining only the essential components that contribute to accurate wireless communication performance. This extraction reduces device complexity while preserving model accuracy
Solution Approach 2:
The patent transforms the model parameters from high-dimensional complex representations to low-dimensional simplified representations. By changing the parameter structure and dimensionality, the system achieves comparable accuracy with reduced model complexity and lower device requirements
3Reliability
If high-dimensional data is used for training to ensure comprehensive information coverage, then model completeness is improved, but training speed deteriorates
Solution Approach 1:
The patent extracts the most informative features from the high-dimensional data while removing redundant dimensions. This extraction maintains the completeness of essential information needed for reliable model training while significantly reducing the data volume that requires processing, thereby improving training speed
Solution Approach 2:
The patent transforms the data from high-dimensional space to low-dimensional space by identifying the essential dimensions that capture the core information. This dimensionality change preserves model completeness by retaining the most significant features while reducing the overall dimensional complexity to accelerate training
Data Source
AI summary
Disclosed are a training method, a method for using a model, a wireless communication method, and an apparatus. The training method includes: generating, by a first device, a second data set according to a first data set, where data in the second data set is low-dimensional representation data of data in the first data set; and training, by the first device according to the second data set, a first model used for wireless communication.