Machine Learning Constellation Correction for Wireless Demodulation
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Solution Overview
Problem
Wireless devices face performance issues due to errors in data transmission caused by wireless channel conditions, which often require retransmission, leading to increased resource expenditure and reduced spectral efficiency.
Innovation Solution
A correction service utilizing machine learning logic that generates a corrective signal constellation matrix by analyzing error vector magnitude data and channel information to minimize retransmissions and improve data decoding accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error correction techniques and retransmission mechanisms are used, then data transmission reliability is maintained, but spectral efficiency deteriorates due to increased resource expenditure
Solution Approach 1:
The patent modifies the signal constellation parameters dynamically by generating a corrected signal constellation matrix that adjusts constellation point positions based on detected error patterns. This parameter change allows the system to adapt to channel conditions and correct errors without requiring retransmission, thereby maintaining reliability while improving spectral efficiency
Solution Approach 2:
The patent implements a feedback mechanism where the receiver detects demodulation errors, generates error vector magnitude data, and feeds this information back to correct the signal constellation. This closed-loop feedback enables continuous adaptation to channel conditions, reducing retransmissions while maintaining transmission reliability
2Reliability
If retransmission mechanisms are employed to correct uncorrectable errors, then data accuracy is maintained, but resource expenditure increases
Solution Approach 1:
The patent performs preliminary error correction by generating a corrected signal constellation matrix before data transmission occurs or at the receiver end before final decoding. This preliminary action identifies and corrects potential errors in advance, preventing the need for retransmission and reducing resource expenditure while maintaining data accuracy
Solution Approach 2:
The patent converts the harmful effect of channel impairments and interference into beneficial error pattern information. By analyzing error vector magnitude data and using it to correct the signal constellation, the system transforms the information about transmission errors into a mechanism for preventing future errors, thereby reducing retransmissions and resource usage
3Productivity
If machine learning logic is implemented to generate corrective signal constellation matrices, then spectral efficiency improves by reducing retransmissions, but device complexity increases
Solution Approach 1:
The patent implements self-service by enabling the wireless device to automatically detect error patterns, generate error vector magnitude data, and correct its own signal constellation without external intervention. This self-correcting mechanism reduces the need for complex network-side processing and minimizes device complexity while improving spectral efficiency through reduced retransmissions
Data Source
AI summary
A method, a device, and a non-transitory storage medium having instructions to receive data and first channel information pertaining to a transmission of the data; store first constellation data and demodulate the data; determine whether any error exists pertaining to the data; receive retransmitted data and second channel information when an error exists; demodulate the retransmitted data; calculate error vector magnitude data; generate corrective constellation data based on the error vector magnitude data, the first channel information, and the second channel information, wherein the corrective constellation data includes at least one reference constellation point that is repositioned on a constellation plane relative to at least one corresponding reference constellation point of a default constellation data; and use the corrective constellation data when demodulating additional data that is subsequently received.