AI Wireless Receiver for Real-Time Message Fault Correction
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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 identification and correction methods.
Innovation Solution
A wireless receiver equipped with an AI model that analyzes waveform data and error-detection codes to identify and correct faults in real-time, using parameters such as amplitude and phase fluctuations, modulation deviations, and error-detection code correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transmission power is increased to improve reception, then signal quality improves, but network interference increases and the approach becomes futile
Solution Approach 1:
The patent replaces the mechanical approach of increasing transmission power with an information-processing approach using AI models and error-detection codes to identify and correct faults in received messages, achieving improved reception quality without increasing power or causing additional interference
Solution Approach 2:
The patent introduces error-detection codes and AI-based fault identification as intermediary mechanisms between transmitter and receiver, enabling reliable communication through fault correction rather than through power-based signal dominance
2Productivity
If fault correction is implemented in real-time, then throughput and reliability improve, but device complexity increases
Solution Approach 1:
The patent implements self-service fault correction where the receiver autonomously identifies and corrects its own faults using embedded error-detection codes and AI models, eliminating the need for complex external intervention or retransmission protocols
Solution Approach 2:
The patent applies preliminary action by embedding error-detection codes and training AI models in advance, enabling the receiver to rapidly identify and correct faults without requiring complex real-time computation or external assistance
3Loss of time
If fault correction is implemented without retransmission, then latency is reduced, but measurement precision of fault identification must be high
Solution Approach 1:
The patent applies beforehand cushioning by embedding error-detection codes and training AI models in advance, creating a buffer of pre-computed knowledge that enables accurate fault identification without requiring time-consuming retransmissions or iterative correction processes
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.