AI Waveform Fault Localization for 5G/6G Message Correction
Find Innovative SolutionsGenerate Solutions
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 error correction methods are complex and costly.
Innovation Solution
A method using an AI model that analyzes waveform data and error-detection codes to identify and correct faults in wireless messages in real-time, allowing for efficient localization and mitigation of message faults without the need for retransmission, even in low-capability IoT receivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transmission power is increased to improve reception in high-frequency wireless communications, then signal strength is improved, but energy consumption increases and network interference worsens
Solution Approach 1:
The patent replaces traditional mechanical/power-based signal strengthening (increasing transmission power) with an AI-based fault detection and correction system that operates at the signal processing level. The AI model analyzes waveform data to identify and correct faults without requiring additional transmission power, thus improving reception reliability while avoiding increased energy consumption and network interference.
2Reliability
If complex error correction methods are used to improve message reliability, then fault detection accuracy is improved, but device complexity and computational cost increase
Solution Approach 1:
The patent extracts and analyzes specific waveform characteristics (amplitude, frequency, phase features) from the received signals to identify faults. By focusing on key waveform attributes rather than processing entire complex error correction codes, the system achieves effective fault detection with reduced computational complexity, making it suitable for resource-constrained IoT devices.
3Productivity
If real-time fault detection is implemented to improve throughput, then latency is reduced, but computational processing requirements increase
Solution Approach 1:
The patent performs preliminary analysis of waveform characteristics during the signal reception phase, extracting amplitude, frequency, and phase features before full message processing. This preliminary fault detection approach enables real-time identification of corrupted messages, allowing for immediate retransmission requests without waiting for complete message decoding, thus reducing latency while managing computational load efficiently.
Data Source
AI summary
Message faults are expected to be an increasing problem in 5G and 6G, due to signal fading at high frequencies, heavy background interference, and high user densities. Retransmissions are expensive in time, power, and the additional background they generate. Prior art includes “soft-combining” among multiple copies, an especially ineffective fault mitigation procedure when SNR is low. Nevertheless, the waveform signals of even badly faulted message elements are rich with information about the correct value. Therefore, procedures are disclosed herein for determining which message elements of a corrupted message, or its associated error-detection code, are faulted, by measuring characteristic parameters of the signal waveform of each message element, and correlating those parameters with the associated error-detection code. In many cases, the corrupted message may be corrected without a retransmission, according to some embodiments.