AI Error Correction for 5G/6G Analog-Digital Signal Faults
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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 correction methods are complex and costly.
Innovation Solution
A method using an AI model to demodulate messages by correlating waveform data with error-detection codes to identify and correct faults in real-time, allowing for rapid localization and mitigation of corrupted message elements without the need for retransmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Strength
If increasing transmission power is used to improve reception, then signal strength is improved, but network interference increases and the approach becomes futile in crowded network spaces
Solution Approach 1:
The patent replaces the mechanical approach of increasing transmission power with an AI-based signal processing system. The neural network model processes received signals to extract meaningful information while filtering out interference, substituting brute-force power increases with intelligent signal analysis that operates effectively in crowded spectral environments.
Solution Approach 2:
The patent changes the parameter being optimized from transmission power to signal processing complexity. Instead of adjusting power levels, the system uses a neural network with multiple layers and parameters (weights, biases, activation functions) to adaptively process signals and extract information despite interference and fading conditions.
2Reliability
If complex fault correction methods are used to improve reliability, then message accuracy is improved, but computational complexity and cost increase
Solution Approach 1:
The patent implements self-service through the AI model that automatically detects, localizes, and corrects faults in received messages without requiring complex external error correction protocols. The neural network performs fault mitigation autonomously by learning from training data, eliminating the need for sophisticated retransmission protocols or complex forward error correction codes.
Solution Approach 2:
The patent uses copying by training the neural network model on numerous examples of corrupted and clean messages. The model learns to replicate the characteristics of fault-free messages by processing many training samples, effectively copying the desired output pattern and applying it to correct faults in real-time communication without requiring complex real-time computation.
3Reliability
If retransmission is used to correct corrupted messages, then reliability is improved, but transmission delays and throughput loss increase
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network model on extensive datasets of corrupted messages before deployment. This preliminary training equips the model with fault mitigation capabilities that enable it to correct errors in real-time without requiring retransmission, thereby preventing time loss rather than remedying it afterward.
Solution Approach 2:
The patent implements skipping by rushing through the fault correction process within the same transmission interval using fast neural network inference. Instead of pausing for retransmission, the system rapidly processes the corrupted signal through the trained model and outputs corrected data, effectively skipping the time-consuming retransmission step entirely.
Data Source
AI summary
Artificial Intelligence (AI) is well-suited to mitigate message faults by combining analog and digital information in 5G and 6G communications. The analog information includes everything measureable about the waveform signal as-received, and the digital information includes the error-detection code accompanying the message. For example, the AI model can localize the most likely faulted message elements according to amplitude fluctuations or phase deviations or other signaling irregularity, and can then use the error-detection code to calculate the corrected values of the faulted message elements. The AI model can also check the error-detection code itself for faults and consistency, as well as a demodulation reference that was used to demodulate the message, thereby avoiding a defective mitigation if either of those is faulted. The AI model can provide output including the most likely corrected version of the message, as well as a comparison with other possible versions, if any.