Artificial-intelligence error mitigation 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 according to pulse-amplitude modulation. 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 message resource element modulated according to a modulation scheme, the modulation scheme comprising one or more predetermined amplitude levels and a plurality of states, each state comprising an I-branch signal multiplexed with a Q-branch signal having a phase shift relative to the I-branch signal, the method comprising:
a. using, in a computer, an artificial intelligence array comprising a plurality of input parameters, an output node, and a plurality of internal functions, each internal function depending functionally on one or more of the input parameters, and wherein the output node depends functionally on the internal functions;
b. determining, for each message element of the message, a message element property comprising a modulation quality or an amplitude of a sum-signal or a phase of the sum-signal or a signal-to-noise ratio of the message element or a combination of those, the sum-signal comprising the I-branch signal added to the Q-branch signal;
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 array, an output result 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. an I-branch difference comprising a difference between the amplitude of the I-branch signal and one of the predetermined amplitude levels of the modulation scheme;
b. a Q-branch difference comprising a difference between the amplitude of the Q-branch signal and one of the predetermined amplitude levels of the modulation scheme;
c. the amplitude or the phase of the sum-signal; and
d. the signal-to-noise ratio or a signal-to-interference-and-noise ratio.
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 internal functions according to the input parameters, and then calculating the output result according to the internal functions.
6. The method of claim 1 , wherein the output result comprises a list of predicted probabilities, each probability in the list indicating a probability that one of the message elements is faulted, respectively.
7. The method of claim 1 , further comprising providing, as an input to the artificial intelligence array, a parameter of a demodulation reference, the demodulation reference being 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 an amplitude of the sum-signal or a phase of the sum-signal, 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. determining a modulation quality of each message element of the retransmitted message; and
c. providing, as input to the artificial intelligence array, the modulation quality as determined for each message element of the retransmitted message.
10. The method of claim 9 , further comprising:
a. selecting whichever of the elements of the message and the retransmitted copy, has the better quality, wherein the quality comprises the modulation quality or the signal-to-noise ratio or properties of the sum-signal or a combination thereof; and
b. providing, as further input to the artificial intelligence array, the selected elements.
11. The method of claim 1 , further comprising determining an algorithm wherein:
a. the algorithm is based at least in part on the artificial intelligence array;
b. the algorithm is configured to predict which message elements, of a corrupted message, are faulted; and
c. the algorithm includes fewer inputs than the artificial intelligence array and fewer internal functions than the artificial intelligence array.
12. The method of claim 11 , wherein the algorithm is at least one of:
a. the artificial intelligence array with at least one input removed and at least one internal function removed;
b. an analytic formula;
c. non-transitory computer-readable medium containing instructions comprising a computer program;
d. a table or array; and
e. combinations thereof.
13. The method of claim 1 , further comprising:
a. producing a first algorithm based at least in part on the artificial intelligence array, the first algorithm configured to predict the output result based at least in part on the input parameters;
b. transmitting the first algorithm to a network or a base station of the network;
c. producing a second algorithm based at least in part on the first algorithm; the second algorithm having fewer inputs than the first algorithm; and
d. transmitting the second algorithm to a user device.
14. The method of claim 13 , further comprising:
a. receiving, by the network or the base station, the first algorithm;
b. predicting, by the network or the base station, according to the first algorithm, which message elements of a message, from the user device, are faulted;
c. receiving, by the user device, the second algorithm; and
d. predicting, by the user device, according to the second algorithm, which message elements of a message, from the network or the base station, are faulted.