Neural Generation Models for Adaptive Wireless Information Inference
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Solution Overview
Problem
Existing wireless communication technologies fail to fully utilize channel estimation information for adaptive communication, particularly in diverse scenarios such as self-driving and augmented reality, leading to suboptimal performance and lack of intelligence in communication networks.
Innovation Solution
Implement a method and apparatus that utilize information collected by communication devices to train generation models, enabling communication-assisted detection and detection-assisted communication by generating or inferring additional information using neural networks, and evolve these models into all-scenario generation models through continuous summarization and retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If channel estimation information is not fully utilized, then communication devices can operate with simpler processing, but communication network intelligence and adaptability deteriorate
Solution Approach 1:
The patent introduces a generation model (neural network) as an intermediary that processes channel estimation information and generates enhanced communication parameters. This mediator transforms raw channel data into actionable insights, enabling adaptive communication without requiring complex processing at every device stage.
Solution Approach 2:
The generation model is trained in advance using historical channel estimation data and communication performance data. This preliminary training phase allows the model to learn optimal mappings between channel conditions and communication parameters, so that during actual operation, the model can quickly generate recommendations without real-time complex computation.
2Reliability
If scenario-specific generation models are used, then communication performance in specific scenarios improves, but device complexity and model management burden increase
Solution Approach 1:
The patent creates a universal generation model that can handle multiple communication scenarios through unified training. Instead of maintaining separate models for different scenarios, the model is trained on diverse channel estimation data from various scenarios, enabling it to generalize and provide reliable performance across different conditions with a single model instance.
Solution Approach 2:
The patent merges training data from multiple scenarios and communication parameters into a unified training set. By combining channel estimation information with various communication metrics (throughput, latency, error rates) across different scenarios during training, the generation model learns comprehensive patterns that apply universally, reducing the need for multiple specialized models.
3Measurement precision
If more wireless information is collected, then communication-assisted detection capability improves, but information processing load and training data requirements increase
Solution Approach 1:
The patent extracts only the most relevant features from collected wireless information for training the generation model. Instead of using all raw channel estimation data, the system identifies and extracts key channel characteristics that have the strongest correlation with communication performance, reducing training data volume while maintaining detection precision.
Solution Approach 2:
The patent transforms raw channel estimation information into optimized feature representations that are more suitable for training. By changing the parameter representation (e.g., selecting specific channel metrics, normalizing data, creating derived features), the system reduces the effective data volume needed for training while enhancing the information quality for detection.
Data Source
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AI summary
Embodiments of this application disclose an information generation method and a related apparatus. The method includes: A second device receives a first message and a third message, and sends a second message to a first device. The first message indicates all or a part of a first generator, the third message indicates all or a part of a third generator, an input supported by the first generator includes first information of a first type, an input supported by the third generator includes fourth information of the first type, and the first generator and the third generator are configured to train a neural network corresponding to a second generator; and the second message indicates all or a part of the second generator, and an input supported by the second generator includes the first information and the fourth information. According to embodiments of this application, information collected in a real scenario may be used to train a generation model, to implement communication-assisted detection and detection-assisted communication, so that a communication network develops towards a more intelligent and adaptive direction.