AI-based error detection and correction in 5G/6G messaging
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.
1. A method for predicting which message elements in a corrupted message are faulted, each message element comprising a modulated resource element, the method comprising:
a. using, in a computer, an artificial intelligence array comprising a plurality of input parameters, an output, and a plurality of intermediate functions, each intermediate function depending functionally on one or more of the input parameters, and wherein the output depends functionally on the intermediate functions;
b. determining, for each message element of the message, a message element property comprising a modulation quality or a signal-to-noise ratio of the message element or a combination of those;
c. determining, for each message element of the message, a background property comprising noise or interference or a combination of those;
d. providing, as input parameters to the artificial intelligence array, the message element property and the background property associated with each message element; and
e. determining, with the artificial intelligence structure, the output comprising an indication of one or more message elements of the message predicted to be faulted.
2. The method of claim 1 , wherein the message complies with 5G or 6G technology.
3. The method of claim 1 , wherein the modulation quality is related to a difference between a modulation of the message element and a phase or amplitude level of a modulation scheme.
4. The method of claim 1 , wherein the background property comprises a noise level or an interference level, or a combination thereof.
5. The method of claim 1 , wherein the predicting comprises calculating each of the intermediate functions according to the input parameters, and then calculating the output parameter according to the intermediate functions.
6. The method of claim 1 , wherein the output comprises a list of message elements that are predicted, by the artificial intelligence array, to be faulted.
7. The method of claim 1 , further comprising providing, as an input to the artificial intelligence array, a demodulation reference parameter associated with the message.
8. The method of claim 1 , further comprising:
a. receiving a retransmitted copy of the message;
b. determining, for each element of the retransmitted copy, a retransmission property comprising a modulation quality or a signal-to-noise ratio, or a combination thereof, for each element of the retransmitted copy; and
c. providing, as an input to the artificial intelligence array, the retransmission property according to each element of the retransmitted copy.
9. The method of claim 1 , further comprising:
a. receiving a retransmitted copy of the message;
b. providing, as input to the artificial intelligence array, a merged copy of the message, the merged copy comprising elements selected from the message elements and corresponding elements of the retransmitted copy.
10. The method of claim 9 , wherein the selected elements comprise whichever of the corresponding elements of the message and the retransmitted copy, has the better quality, wherein the quality comprises a modulation quality or a signal-to-noise ratio or a combination thereof.
11. The method of claim 1 , further comprising determining an algorithm, based at least in part on the artificial intelligence array, and configured to predict which message elements, of a corrupted message, are predicted to be faulted.
12. The method of claim 1 , further comprising:
a. producing an algorithm based at least in part on the artificial intelligence array, the algorithm configured to predict the output based at least in part on the input parameters; and
b. transmitting the algorithm to the network.
13. The method of claim 12 , further comprising:
a. receiving, by a network or a user device, the algorithm; and
b. predicting, according to the algorithm, which message elements are faulted.