AI-Based Error Detection and Correction in 5G/6G Messaging

摘要

Artificial intelligence procedures are disclosed for localizing faults in corrupted messages in 5G and 6G, and for correcting those faults based on measured parameters such as backgrounds and message signals. Message faults can be caused by noise or interference from a variety of sources with a wide range of properties. An AI model with multiple adjustable variables may be “trained” using a large number of message events, including faulted messages, to determine which message elements are likely faulted, based on input parameters such as modulation quality, SNR, and other signal properties. The receiving entity can then attempt a grid search to correct the faulted message elements, or request a retransmission. For field use by base stations and user devices, an algorithm may be developed based on the AI model, and configured to predict which message elements are likely faulted. By detecting and correcting message faults, networks may increase reliability and reduce latency while avoiding most retransmission costs and delays, according to some embodiments.