Convolutional Encoder-Decoder for Channel Frequency Response Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current techniques for estimating channel frequency response using echo signals are limited and require further development, especially in accurately determining channel characteristics and updating weights for improved performance.
Innovation Solution
An apparatus and method utilizing a convolutional encoder-decoder with trainable weights to combine features from echo signals, generating an estimate of the channel frequency response, and updating weights based on loss estimation using techniques like backpropagation and hybrid error calculation, incorporating data preprocessing and machine learning for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional echo signal processing methods are used, then the basic channel frequency response estimation can be achieved, but the accuracy and adaptability are limited
Solution Approach 1:
The patent implements feedback through weight updates using backpropagation and hybrid error calculation. The system continuously estimates loss based on the difference between predicted and actual channel frequency responses, then updates the convolutional encoder-decoder weights to minimize this loss, creating a closed-loop feedback mechanism that improves accuracy and adaptability over time
Solution Approach 2:
The patent changes parameters by training the convolutional encoder-decoder with adjustable weights that adapt to different channel conditions. The hybrid error function modifies the loss calculation by incorporating weighted sums of root-mean square error, local maximum, and derivative weighting, allowing the system to adjust parameters dynamically for optimal performance across varying channel characteristics
2Measurement precision
If machine learning with weight updates is implemented, then estimation accuracy improves, but device complexity increases
Solution Approach 1:
The patent replaces traditional mechanical signal processing mechanisms with machine learning-based convolutional neural networks. Instead of using complex analog signal processing hardware, the system uses software-based convolutional encoder-decoder models that can be trained and updated, simplifying the physical implementation while maintaining high accuracy
Solution Approach 2:
The convolutional encoder-decoder model serves multiple functions: feature extraction, channel frequency response estimation, and adaptive weight updates. This multi-functional approach consolidates what would otherwise require separate processing stages into a single unified model, reducing overall system complexity
Data Source
AI summary
An apparatus, method and computer program is described comprising: combining first features extracted from an echo signal using a convolutional encoder of a convolutional encoder-decoder having first weights, wherein the echo signal is obtained in response to a transmission over a channel or a simulation thereof; and using a convolutional decoder of the convolutional encoder-decoder to generate an estimate of a frequency response of the channel based on the echo signal, wherein the convolutional decoder has second weights.