AI-Based 5G/6G Message Recovery Without Retransmission
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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, requiring efficient fault detection and correction methods that can be implemented by low-complexity IoT receivers without incurring delays or costs associated with retransmissions.
Innovation Solution
An AI model is used to demodulate messages, identify corrupted components, and generate candidate versions with likelihood values, enabling real-time fault correction by correlating waveform data with error-detection codes, thereby localizing and mitigating faults without the need for retransmissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transmission power is increased to improve reception reliability, then signal quality improves, but network interference increases and the solution becomes futile
Solution Approach 1:
The patent converts the harmful effect of network interference into a useful signal quality indicator. By having each receiver measure the received signal strength indicator (RSSI) of incoming messages and report these measurements to a network entity, the interference that would normally degrade communication becomes data that can be used to identify faulty transmitters. The network entity uses these RSSI measurements to detect abnormal patterns and locate malfunctioning devices, thereby transforming the harmful interference into a diagnostic tool for fault management.
2Reliability
If retransmission is used to correct message faults, then message reliability improves, but transmission delay increases
Solution Approach 1:
The patent implements preliminary fault detection by having receivers measure and report RSSI values before actual message transmission occurs. The network entity analyzes these pre-transmission measurements to identify potential faulty transmitters in advance. By detecting and isolating faulty devices before they can corrupt message transmissions, the system prevents errors from occurring in the first place, thereby eliminating the need for time-consuming retransmissions and reducing overall transmission delay.
3Reliability
If complex fault detection and correction procedures are implemented, then message reliability improves, but receiver complexity increases
Solution Approach 1:
The patent enables receivers to perform self-diagnosis and self-reporting of signal quality conditions. Each receiver autonomously measures the RSSI of incoming messages and automatically reports these measurements to the network entity without requiring complex centralized control or sophisticated local processing. This self-service approach allows simple IoT receivers to contribute to network-wide fault detection while maintaining their own low complexity, as the heavy analytical work is performed by the network entity that has access to measurements from all receivers.
Data Source
AI summary
A receiver may use a trained AI model to recover a faulted 5G/6G message by interpreting the meaning or intent of the message by correlating the message content with one of the “expected” message types. For example, the AI model may consider changes to the message, for consistency with an associated error-detection code, thereby producing a series of candidate messages. The AI model can then determine a likelihood that each of the candidate messages is correct, in the context of the receiver (such as an action or condition of the receiver, or a planned activity of the receiver) or is commonly received in that context. For example, the AI model can be trained to recognize the expected messages or message types, and thereby indicate which candidate message has the highest likelihood of being correct. The AI model may also consider waveform parameters to identify likely faults.