Channel Information Generation via Encoder-Decoder Sampling
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Solution Overview
Problem
Manually collecting large amounts of parameter and channel information is inefficient, and existing data enhancement methods for deep learning-based positioning, such as GAN-based approaches, introduce errors and require complex processing.
Innovation Solution
A method involving a trained decoder that outputs channel information by sampling a probability distribution of a hidden variable output by a trained encoder, eliminating the need for manual data collection and reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual collection of parameter information and channel information is used, then data accuracy can be maintained, but collection efficiency is low and time-consuming
Solution Approach 1:
The patent uses a pre-trained encoder-decoder model to generate synthetic channel information that copies the statistical characteristics and patterns of real channel data. The encoder processes parameter information to create hidden representations, and the decoder reconstructs channel information from these representations, producing synthetic data that mimics real channel conditions without requiring manual collection
Solution Approach 2:
The system enables self-service data generation where the encoder-decoder model automatically generates channel information from parameter information without human intervention. The pre-trained model serves itself to produce synthetic training data, eliminating the need for manual data collection and annotation processes
2Quantity of substance
If GAN-based data enhancement methods are used, then data quantity can be increased, but error introduction and processing complexity increase
Solution Approach 1:
The patent extracts only the essential encoding-decoding functionality needed for data enhancement, removing the complex adversarial training mechanisms of GANs. By using a standalone encoder-decoder model pre-trained on real data, the system achieves data generation without the computational overhead and training complexity of GAN architectures
Solution Approach 2:
The patent employs a simpler, more computationally efficient encoder-decoder approach compared to GANs, sacrificing some of the advanced generative capabilities of GANs in exchange for reduced processing complexity and easier implementation. The model generates sufficient synthetic data for training purposes without requiring the complex infrastructure of GAN training
3Quantity of substance
If GAN-based data enhancement methods are used, then data quantity can be increased, but error rates in generated data increase
Solution Approach 1:
The patent performs preliminary training of the encoder-decoder model on real channel data before using it for synthetic data generation. This pre-training phase allows the model to learn accurate representations of real channel characteristics, ensuring that subsequently generated synthetic data maintains high fidelity and accuracy without the error propagation issues seen in GAN-based approaches
Data Source
AI summary
A channel information determination method and apparatus, and a medium are provided. The method includes: inputting a first sampling result and set parameter information into a trained first decoder, so that the trained first decoder outputs first channel information corresponding to the set parameter information, wherein the first sampling result is determined by means of sampling a first probability distribution of a hidden variable, which is output by a trained first encoder.