Adaptive diagnostics for communication systems

1. A network device comprising:

a communication interface comprising a digital signal processor for transmitting data and an application specific integrated circuit for receiving data, the communication interface configured to receive a signal via a communication link and measure a plurality of parameters related to transmission of the signal to provide a plurality of vectors indicative of the plurality of parameters;

an inference module configured to

weight each of the plurality of vectors for a plurality of possible failures such that ones of the plurality of possible failures are each allocated a respective set of weighted vectors, and

generate each of a plurality of probability values based on a machine learning classification model of a network system and a respective one of the sets of weighted vectors, the network system comprising the network device, each of the plurality of probability values indicating a probability that a respective one of the plurality of possible failures has occurred at a respective location among a plurality of locations in the network system, and each of the plurality of possible failures corresponds to a different one of the plurality of locations; and

a control module configured, based on the plurality of probability values, to perform a countermeasure to at least one of improve transmission performance associated with the communication link or correct a failure associated with transmission of the signal via the communication link.

2. The network device of claim 1 , wherein:

the communication link comprises an optical communication link; and

the inference module is configured to generate one of the plurality of parameters based on a 4-level pulse amplitude modulation (PAM4) histogram.

3. The network device of claim 1 , wherein:

the inference module is configured to generate the plurality of probability values based on a softmax regression model; and

the machine learning classification model comprises the softmax regression model.

4. The network device of claim 1 , wherein:

the inference module is configured to generate the plurality of probability values based on a decision tree model; and

the machine learning classification model comprises the decision tree model.

5. The network device of claim 1 , wherein:

the network device is in communication with another network device via the communication link; and

the control module is configured to:

in response to the plurality of probability values, determine that the failure associated with transmission of the signal exists at the another network device, wherein the failure associated with the transmission of the signal refers to the another network device exhibiting at least one of a coding error, a transmission error, an error associated with overheating, an error associated with humidity, or an error associated with silicon failure, and

generate an algorithm to repair the another network device.

6. The network device of claim 1 , wherein:

the inference module is configured to generate the plurality of probability values based on a neural network, and

the machine learning classification model comprises the neural network.

7. The network device of claim 1 , wherein:

the network device is in communication with another network device via the communication link; and

the control module is configured to

in response to the plurality of probability values, determine that a failure exists at the another network device, and

initiate execution of an algorithm to repair the another network device.

8. The network device of claim 1 , wherein the inference module is configured to update the machine learning classification model based on the plurality of parameters.

9. The network device of claim 1 , wherein:

the inference module is configured to, based on the plurality of probability values, determine a receiving failure exists at the network device; and

the control module is configured to generate an algorithm to repair the receiving failure.

10. The network device of claim 1 , wherein:

the inference module is configured to, based on the plurality of probability values, determine that a failure exists at a particular device, the particular device being (i) the network device, (ii) a link between the network device and another network device, or (iii) the another network device; and

the control module is configured to operate in a debugging mode to debug the failure at the particular device and generate a test pattern for diagnosing the failure.

11. The network device of claim 1 , wherein the control module is configured to perform the countermeasure including generating an alarm signal indicating the plurality of probability values, and transmit the alarm signal to a network management system separate from the network device.

12. The network device of claim 1 , wherein the inference module is configured to select:

the plurality of vectors include a first plurality of vectors or a second plurality of vectors;

a first plurality of parameters to measure and provide the first plurality of vectors when the communication link is an optical communication link; and

a second plurality of parameters to measure and provide the second plurality of vectors when the communication link is an electrical communication link.

13. The network device of claim 1 , wherein the inference module is configured to:

weight each of the plurality of vectors to generate another set of weighted vectors; and

generate another probability value based on the machine learning classification model and the another set of weighted vectors, the another probability value indicative of whether no failure has occurred in the network system.

14. The network device of claim 1 , wherein the inference module is configured to generate the plurality of probability values for the plurality of locations, and the plurality of locations refer to (i) the network device, (ii) a link between the network device and another network device, and (iii) the another network device.

15. The network device of claim 1 , wherein the inference module is configured to:

iteratively measure the plurality of parameters; and

for each iteration,

update the machine learning classification model based on a latest measured set of the plurality of parameters, and

update the plurality of probability values based on the updated machine learning classification model.

16. The network device of claim 1 , wherein the inference module is configured to:

iteratively measure the plurality of parameters; and

for each iteration,

update weights of the sets of weighted vectors based on a latest measured set of the plurality of parameters, and

update the plurality of probability values based on the updated weights of the sets of weighted vectors.

17. A diagnostic method comprising:

receiving at a network device a signal via a communication link;

measuring a plurality of parameters related to transmission of the signal to provide a plurality of vectors indicative of the plurality of parameters;

weighting each of the plurality of vectors for a plurality of possible failures such that each of the plurality of possible failures is allocated a respective set of weighted vectors;

generating each of a plurality of probability values based on a machine learning classification model of a network system and a respective one of the sets of weighted vectors, the network system comprising the network device, each of the plurality of probability values indicating a probability that a respective one of the plurality of possible failures has occurred at a respective one of a plurality of locations in the network system, and each of the plurality of possible failures corresponding to a different one of the plurality of locations; and

based on the plurality of probability values, performing a countermeasure to at least one of improve transmission performance associated with the communication link or correct a failure associated with transmission of the signal via the communication link.

18. The method of claim 17 , further comprising:

measuring again the plurality of parameters to provide an updated plurality of vectors;

weighting each of the updated plurality of vectors for the plurality of possible failures such that each of the plurality of possible failures is allocated a respective updated set of weighted vectors; and

generating each of a plurality of updated probability values based on the machine learning classification model and a respective one of the updated sets of weighted vectors.

19. The method of claim 17 , further comprising:

iteratively performing a machine learning algorithm to update the machine learning classification model;

during a first iteration of the machine learning algorithm, weighting the plurality of vectors with preset initial weight values; and

during subsequent iterations of the machine learning algorithm, weighting a plurality of updated vectors based on an updated set of weight values for that iteration.

20. The method of claim 17 , further comprising:

determining a receiver failure of the network device based on the plurality of probability values; and

executing a repairing algorithm to correct the receiver failure.