Method, device, and storage medium for communication waveform performance predication

1. A method for performance prediction of a communication waveform in a communication system, comprising:

measuring, by a receiver, an actual SNR distribution of a communication link between a transmitter and the receiver;

evaluating, by a waveform performance prediction device, a normalized minimum SNR shift required for the communication waveform to operate, wherein the normalized minimum SNR shift is obtained based on a normalized SNR distribution using a neural network (NN), the normalized SNR distribution corresponding to the actual SNR distribution; and

according to the normalized minimum SNR shift, obtaining, by a waveform performance prediction device, an actual minimum SNR shift for the actual SNR distribution, wherein according to the actual minimum SNR shift, the communication system is adjusted for operation.

2. The method according to claim 1 , wherein:

an NN input of the neural network is the normalized SNR distribution; and

an NN output of the neural network is the normalized minimum SNR shift required for the communication waveform to operate.

3. The method according to claim 2 , wherein:

the NN input corresponds to probability masses of a plurality of SNR levels over an SNR range; and

the NN output is the normalized minimum SNR shift corresponding to the NN input.

4. The method according to claim 1 , further including:

generating training samples for the communication waveform by evaluating normalized minimum SNR shifts for a plurality of normalized SNR distributions; and

training the NN with the training samples by reducing a training loss.

5. The method according to claim 4 , wherein:

each training sample includes a normalized SNR distribution and a corresponding normalized minimum SNR shift required for the communication waveform to operate.

6. The method according to claim 5 , further including generating each training sample by performing:

generating a plurality of link conditions, each link condition corresponding to the normalized SNR distribution and an SNR shift of a plurality of SNR shifts; and

evaluating the performance of the communication waveform using each of the plurality of link conditions to find a minimum SNR shift from the plurality of SNR shifts, wherein the minimum SNR shift is determined as the normalized minimum SNR shift required for the communication waveform to operate.

7. The method according to claim 1 , wherein:

the communication system is adjusted by changing a transmission power and/or by selecting another communication waveform for operation.

8. A device for performance prediction of a communication waveform over a communication link, comprising:

a memory, configured to store program instructions for performing a method for performance prediction of a communication waveform in a communication system; and

a processor, coupled with the memory and, when executing the program instructions, configured to:

measure, by a receiver, an actual SNR distribution of a communication link between a transmitter and the receiver;

evaluate, by a waveform performance prediction device, a normalized minimum SNR shift required for the communication waveform to operate, wherein the normalized minimum SNR shift is obtained based on a normalized SNR distribution using a neural network (NN), the normalized SNR distribution corresponding to the actual SNR distribution; and

according to the normalized minimum SNR shift, obtain, by a waveform performance prediction device, an actual minimum SNR shift for the actual SNR distribution, wherein according to the actual minimum SNR shift, the communication system is adjusted for operation.

9. The device according to claim 8 , wherein:

an NN input of the neural network is the normalized SNR distribution; and

an NN output of the neural network is the normalized minimum SNR shift required for the communication waveform to operate.

10. The device according to claim 9 , wherein:

the NN input corresponds to probability masses of a plurality of SNR levels over an SNR range; and

the NN output is the normalized minimum SNR shift corresponding to the NN input.

11. The device according to claim 8 , wherein the processor is further configured to:

generate training samples for the communication waveform by evaluating normalized minimum SNR shifts for a plurality of normalized SNR distributions; and

train the NN with the training samples by reducing a training loss.

12. The device according to claim 11 , wherein:

each training sample includes a normalized SNR distribution and a corresponding normalized minimum SNR shift required for the communication waveform to operate.

13. The device according to claim 12 , wherein the processor is further configured to generate each training sample by performing:

generating a plurality of link conditions, each link condition corresponding to the normalized SNR distribution and an SNR shift of a plurality of SNR shifts; and

evaluating the performance of the communication waveform using each of the plurality of link conditions to find a minimum SNR shift from the plurality of SNR shifts, wherein the minimum SNR shift is determined as the normalized minimum SNR shift required for the communication waveform to operate.

14. The device according to claim 8 , wherein:

the communication system is adjusted by changing a transmission power and/or by selecting another communication waveform for operation.

15. A non-transitory computer-readable storage medium, containing program instructions for, when being executed by a processor, performing a method for performance prediction of a communication waveform in a communication system, the method comprising:

measuring, by a receiver, an actual SNR distribution of a communication link between a transmitter and the receiver;

evaluating, by a waveform performance prediction device, a normalized minimum SNR shift required for the communication waveform to operate, wherein the normalized minimum SNR shift is obtained based on a normalized SNR distribution using a neural network (NN), the normalized SNR distribution corresponding to the actual SNR distribution; and

according to the normalized minimum SNR shift, obtaining, by a waveform performance prediction device, an actual minimum SNR shift for the actual SNR distribution, wherein according to the actual minimum SNR shift, the communication system is adjusted for operation.

16. The storage medium according to claim 15 , wherein:

an NN input of the neural network is the normalized SNR distribution; and

an NN output of the neural network is the normalized minimum SNR shift required for the communication waveform to operate.

17. The storage medium according to claim 15 , wherein:

the NN input corresponds to probability masses of a plurality of SNR levels over an SNR range; and

the NN output is the normalized minimum SNR shift corresponding to the NN input.

18. The storage medium according to claim 15 , wherein the method further includes:

generating training samples for the communication waveform by evaluating normalized minimum SNR shifts for a plurality of normalized SNR distributions; and

training the NN with the training samples by reducing a training loss.

19. The storage medium according to claim 18 , wherein:

each training sample includes a normalized SNR distribution and a corresponding normalized minimum SNR shift required for the communication waveform to operate.

20. The storage medium according to claim 19 , wherein the method further includes generating each training sample by performing:

generating a plurality of link conditions, each link condition corresponding to the normalized SNR distribution and an SNR shift of a plurality of SNR shifts; and

evaluating the performance of the communication waveform using each of the plurality of link conditions to find a minimum SNR shift from the plurality of SNR shifts, wherein the minimum SNR shift is determined as the normalized minimum SNR shift required for the communication waveform to operate.