AI Waveform Fault Correction in 5G/6G Receivers
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Solution Overview
Problem
Current wireless communication systems face challenges in localizing and mitigating faults in messages, particularly in high-density urban and industrial settings, where error-correction codes are inefficient and often require costly retransmissions.
Innovation Solution
A method using artificial intelligence (AI) to analyze waveform signals for fault determination, where the AI model processes data on waveform fluctuations, modulation deviations, and error-detection codes to identify and correct faulted message elements without the need for retransmissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If error-correction codes are added to the message, then reliability is improved, but device complexity and resource consumption increase
Solution Approach 1:
The patent extracts the fault localization function from traditional error-correction codes, separating detection (via lightweight codes) from correction (via AI waveform analysis). This removes the need for bulky FEC codes while maintaining reliability through AI-based localization and repair of faulted message elements.
Solution Approach 2:
The patent introduces an AI model as an intermediary between the received message and the correction process. The AI model analyzes waveform signals to identify and locate faults, then determines corrected values, replacing the traditional direct error-correction code approach and reducing overall system complexity.
2Reliability
If traditional error-correction codes are used, then reliability is improved, but loss of time increases due to retransmission requirements
Solution Approach 1:
The patent performs preliminary fault localization and correction using AI waveform analysis before retransmission is needed. By detecting and correcting faults in real-time at the receiver, the system eliminates the need for time-consuming retransmission cycles, significantly reducing time loss while maintaining high reliability.
3Reliability
If error-correction codes are added to the message, then reliability is improved, but use of energy increases
Solution Approach 1:
The patent extracts the energy-intensive error-correction function from traditional FEC codes and replaces it with AI-based waveform analysis. The lightweight error-detection codes consume minimal energy, while the AI model processes waveform signals to locate and correct faults, reducing overall energy consumption compared to bulky error-correction codes.
4Reliability
If retransmission is requested for faulted messages, then reliability is improved, but productivity decreases due to time and resource costs
Solution Approach 1:
The patent performs preliminary fault localization and correction using AI waveform analysis before retransmission is needed. By detecting and correcting faults in real-time at the receiver, the system eliminates the need for time-consuming retransmission cycles, significantly reducing time loss while maintaining high reliability.
Solution Approach 2:
The patent enables the receiving entity to self-correct faulted message elements using AI-based waveform analysis and error-detection codes. This self-service capability eliminates the need for external retransmission assistance, improving communication efficiency and productivity while maintaining reliability.
Data Source
AI summary
An unsolved problem in 5G-Advanced and especially 6G is message fault mitigation without a costly retransmission. Methods are disclosed for the receiver to analyze each message element's received waveform signal to detect characteristic features of interference and noise, such as excessive amplitude or phase variation within the message element or excessive deviation from the predetermined modulation levels of the modulation scheme, and to provide that data to an AI model trained in message fault correction. The AI model can then identify the faulted message elements, and attempt to correct them according to the likely intent or meaning of the message based on the non-faulted message elements, and on the bit sequences of previously received non-faulted messages, and other criteria that the AI model may apply. By repairing the message upon receipt, the costs in time, transmission power, and background noise generation may be avoided. Next-generation users will enjoy the improved reception.