AI Receiver Fault Mitigation for Corrupted 5G/6G Messages
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Solution Overview
Problem
Next-generation wireless communications face challenges in reliability due to signal fading at high frequencies and interference in crowded network spaces, necessitating efficient fault detection and correction methods.
Innovation Solution
The implementation of an AI model that uses correlations between waveform 'analog' data and error-detection code 'digital' data to identify and correct faults in 5G/6G messages, enabling real-time fault localization and mitigation without the need for retransmissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transmission power is increased to improve reception, then signal strength is improved, but network interference increases and the approach becomes futile
Solution Approach 1:
The patent replaces the mechanical approach of increasing transmission power with an AI-based signal processing system. The AI model analyzes received signals to identify and correct faults, substituting the need for higher power transmission with intelligent post-reception processing that achieves reliable communication without adding interference.
Solution Approach 2:
The patent introduces an AI model as an intermediary between the received signal and the final message interpretation. This intermediary processes the corrupted signal, identifies faults, and reconstructs the intended message, enabling reliable communication without requiring increased transmission power that would cause interference.
2Loss of time
If fault correction is performed without retransmission, then latency is reduced, but fault detection and correction complexity increases
Solution Approach 1:
The patent implements self-service fault correction where the AI model autonomously detects and corrects faults in received messages without requiring external retransmission requests. The system serves itself by automatically identifying corrupted bits and reconstructing the intended message, reducing latency while managing complexity through intelligent automation.
Solution Approach 2:
The patent changes the approach from binary retransmission to probabilistic fault correction by analyzing signal characteristics and using AI to determine the most likely intended message. This parameter change allows for faster correction by working with the received signal directly rather than waiting for retransmission, balancing complexity with performance.
3Reliability
If AI-based fault correction is implemented, then message reliability is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the fault correction process into distinct stages: initial signal reception, AI-based fault detection, fault analysis, and message reconstruction. This segmentation allows the system to manage computational complexity by processing different aspects of fault correction separately rather than requiring all computations simultaneously.
Solution Approach 2:
The patent applies partial action by focusing the AI model's efforts on detecting and correcting only the corrupted portions of the message rather than processing the entire message uniformly. This approach reduces computational complexity by concentrating processing resources on the specific fault areas identified through signal analysis.
Data Source
AI summary
Message faulting is expected to be a major challenge in 5G-Advanced and especially 6G, due to increased pathloss and phase noise at FR2 frequencies, and exponential crowding of networks. Legacy methods for forward-correction or automatic retransmissions are unsuitable to the fast-paced demands of next-generation users. Therefore, disclosed herein is an AI-based receiver that interprets a corrupted message to determine the most likely meaning or intent, and thereby provides one or more candidate corrected messages along with a likelihood that each of the candidate corrected messages is indeed correct. The AI model may also be provided with data on the context or current activity of the receiver, data on the waveform of each message element, and other data available to the receiver, so that the AI model can further refine the likelihood values. By recovering corrupted messages in the receiver, a costly retransmission may be avoided, saving time and resource usage.