AI Waveform Error Correction for 5G/6G Messages
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Solution Overview
Problem
Next-generation wireless communications face challenges in reliably transmitting messages at high frequencies due to signal fading and interference, particularly in crowded network spaces, where increasing transmission power is ineffective and existing fault detection methods are complex and costly.
Innovation Solution
A method using an AI model that correlates waveform data with error-detection codes to identify and correct faults in wireless messages, allowing for real-time fault detection and mitigation without the need for retransmissions, even in low-capability IoT receivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If increasing transmission power is used to improve reception, then signal strength is improved, but energy consumption increases and competing transmitters will follow suit making the approach futile
Solution Approach 1:
The patent replaces the mechanical approach of increasing transmission power with an AI-based signal processing system that analyzes waveform characteristics and error-detection codes to identify and correct faults in received messages, achieving improved reception quality without increasing power consumption
Solution Approach 2:
The patent changes the approach from modifying transmission parameters (power) to analyzing and correcting received signal parameters (waveform characteristics, error-detection codes) through AI processing, enabling fault mitigation without the need for increased transmission power
2Measurement precision
If complex fault detection methods are used to identify message faults, then detection accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent implements a self-service approach where the AI model automatically analyzes error-detection codes and waveform data to identify and correct faults without requiring complex external detection systems, enabling simple IoT receivers to perform fault mitigation independently
Solution Approach 2:
The patent introduces an AI model as an intermediary that processes and correlates error-detection codes with waveform characteristics, simplifying the detection system while maintaining high accuracy through intelligent pattern recognition rather than complex hardware
3Reliability
If retransmission is used to mitigate corrupted messages, then message reliability is improved, but transmission latency and throughput are degraded
Solution Approach 1:
The patent applies preliminary action by proactively analyzing error-detection codes and waveform data to identify and correct faults in the received message before any retransmission is needed, eliminating the time loss associated with retransmission protocols
Solution Approach 2:
The patent converts the potentially harmful effect of message corruption into a benefit by using the error-detection code and waveform analysis to identify and correct faults, transforming what would require a time-consuming retransmission into a rapid local correction process
Data Source
AI summary
Message faulting is a critical unsolved problem for 5G and 6G. Disclosed herein is a method for combining an AI-based analysis of the waveform data of each message element, plus the constraint of an associated error-detection code (such as a CRC or parity construct of the correct message) to localize and, in many cases, correct a limited number of faults per message, without a retransmission. For example, the waveform data may include a deviation of the amplitude or phase of a particular message element, relative to an average of the amplitudes or phases of the other message elements that have the same demodulation value. The outliers are thereby exposed as the most likely faulted message elements. In addition, using the error-detection code, the AI model can determine the most likely corrected message, thereby avoiding retransmission delays and power usage and other costs.