Deep Learning Channel Estimation for Wireless Signal Decoding

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Solution Overview

Problem

In wireless communication systems, reception devices face challenges in decoding signals due to irregular changes in wireless channel states, leading to restricted decoding capabilities when channel estimation fails.

Innovation Solution

The implementation of a channel estimation and equalization module in electronic devices that utilizes deep learning to process received signals, extracting features and estimating channels based on reference signals to restore the original signal effectively.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional channel estimation methods are used, then the decoding process is simple and fast, but the decoding reliability deteriorates when channel estimation fails due to irregular channel changes

Engineering Contradiction:

Improvesignal decoding reliabilityVSAvoidchannel estimation and equalization system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical/channel-based signal processing methods with deep learning neural networks. The neural network model processes received signals directly, extracting features and estimating channels through learned representations rather than conventional mathematical operations, thereby improving reliability in complex channel conditions while managing system complexity through optimized network architecture

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent transforms the channel estimation approach by changing from traditional channel parameters to deep learning-based feature representations. The neural network learns optimal feature extractors that adapt to varying channel conditions, converting the problem into a parameter learning task that can handle irregular channel changes more effectively

Inventive Principle:

Principle #35Parameter changes

2Productivity

If sequential channel estimation and signal decoding operations are performed, then the processing steps are well-defined and simple, but the processing time increases when channel estimation fails requiring repeated attempts

Engineering Contradiction:

Improvesignal processing efficiencyVSAvoidtime for channel estimation and decoding operations

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent merges channel estimation and signal decoding into a unified neural network processing framework. Instead of performing estimation and decoding as separate sequential steps, the neural network simultaneously performs both functions by processing the received signal through shared feature extraction layers, thereby reducing overall processing time and eliminating the need for repeated attempts when estimation fails

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The neural network performs preliminary feature extraction and channel estimation in a single forward pass, preparing both the channel state information and decoded signal in advance. This preliminary processing eliminates the need for iterative refinement and subsequent decoding attempts, significantly reducing time loss when traditional methods fail

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If conventional signal processing methods are used, then the system is easier to implement, but the adaptability to various wireless environments deteriorates

Engineering Contradiction:

Improveadaptation to wireless environmentsVSAvoiddeep learning model complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a universal neural network model that can adapt to various wireless communication scenarios and channel conditions. The model performs multiple functions including feature extraction, channel estimation, and signal decoding within a single integrated architecture, enabling it to handle diverse wireless environments without requiring separate specialized systems for each condition

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The neural network performs self-adaptation to different wireless environments through its learned parameters and feature representations. The model automatically adjusts its internal representations based on the input signal characteristics, enabling it to adapt to varying channel conditions, interference patterns, and modulation schemes without external intervention or manual reconfiguration

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12580795B2Electronic device for processing wireless signal, and operating method therefor

Publication Date: 2026.03.17 SAMSUNG ELECTRONICS CO LTD

AI summary

An electronic device includes at least one antenna, and a channel estimation and equalization module for processing a reception signal received through the at least one antenna. The channel estimation and equalization module may identify the received signal and a reference signal related to the received signal. The channel estimation and equalization module may also, via deep learning based on the received signal and the reference signal: extract features of the received signal and the reference signal, estimate a channel of the received signal, based on the extracted features, and restore a signal corresponding to the received signal.