AI Fault Localization in Wireless Receivers Without Retransmission
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Solution Overview
Problem
Existing wireless communication systems face challenges in localizing and mitigating faults in messages, especially in high-density urban and industrial settings, where error-correction codes are inefficient and often require costly retransmissions.
Innovation Solution
A method using an AI model in wireless receivers to analyze waveform fluctuations and modulation deviations to identify and correct faulted message elements, allowing for real-time fault localization and mitigation without retransmissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If error-correction codes are added to the message, then fault mitigation capability is improved, but resource area consumption increases and fault localization capability deteriorates
Solution Approach 1:
The patent extracts the fault localization function from traditional error-correction codes by using waveform fluctuation analysis. Instead of relying on bulky FEC codes, the system analyzes the waveform characteristics of individual message elements to identify and locate faults, thereby reducing resource consumption while maintaining fault mitigation capability
Solution Approach 2:
The patent introduces waveform fluctuation analysis as an intermediary mechanism between signal reception and fault detection. This intermediary approach allows the system to detect and localize faults by examining fluctuations in waveform amplitude and phase, providing a more efficient alternative to traditional error-correction codes
2Reliability
If error-correction codes are added to the message, then fault mitigation capability is improved, but fault localization precision deteriorates
Solution Approach 1:
The patent segments the message into individual message elements and analyzes the waveform fluctuations of each element separately. This segmentation approach enables precise localization of faults to specific message elements, as each element's waveform characteristics are independently examined for fluctuations indicating faults
Solution Approach 2:
The patent uses waveform fluctuation analysis to detect changes in signal characteristics (analogous to color changes) that indicate faults. By monitoring fluctuations in amplitude and phase waveforms, the system can precisely identify which message elements are faulted, providing both localization precision and mitigation capability
3Reliability
If retransmission is requested for faulted messages, then fault mitigation is achieved, but time consumption and resource cost increase
Solution Approach 1:
The patent performs preliminary fault localization and correction actions by analyzing waveform fluctuations in real-time. Instead of waiting for retransmission, the system identifies faulted message elements and corrects them using the waveform analysis results, thereby eliminating the time loss associated with retransmission cycles
Solution Approach 2:
The patent enables the receiving entity to self-correct faults by using waveform fluctuation analysis to identify and repair faulted message elements locally. This self-service capability eliminates the need for retransmission, as the receiver can independently detect and correct faults using the analyzed waveform characteristics
Data Source
AI summary
A key requirement for 5G and 6G networking is reliability. Message faults are inevitable, and therefore procedures are needed to identify each fault location in a message and, if possible, to rectify it. Disclosed herein are artificial intelligence AI models and procedures for mitigating faults in wireless messages by (a) evaluating the signal quality of each message element according to waveform features and modulation deviations, (b) evaluating the fault probability of each message element by seeking correlations, which may be subtle, among the various waveform measurements including polarization and frequency offset, and (c) correcting the faults according to the message type, apparent format, intent or meaning, typical previous messages of a similar type, correlations of bit patterns and symbol sequences, error-detection codes if present, and other content-based indicators uncovered during model development. Automatic, real-time fault localization and correction may save substantial time and resources while substantially enhancing messaging reliability.