Method and apparatus for estimating optimal optical signal transmission power for transmission network equipment

1 . An apparatus for estimating and controlling optical signal transmission power for transmission network equipment, comprising

a memory configured to store transmission network facility information including at least optical cable length, number of patch sections, number of attenuators, and equipment type;

a communication circuit configured to communicate with at least one transmission network equipment and an element management system (EMS); and

a processor configured to:

generate estimation data including an estimated optical signal transmission power and the transmission network facility information;

input the estimation data into a pre-trained deep learning regression model to calculate an optical signal reception power;

determine an optimal optical signal transmission power based on whether the calculated optical signal reception power satisfies a predetermined reception power condition; and

control, via the communication circuit, a transmitter of the transmission network equipment to output an optical signal having the determined optimal optical signal transmission power.

2 . The apparatus of claim 1 , wherein the processor determines the estimated optical signal transmission power as the optimal optical signal transmission power in an event that the calculated optical signal reception power satisfies a predetermined value.

3 . The apparatus of claim 1 , wherein the processor modifies the estimation data in an event that the calculated optical signal reception power does not satisfy the predetermined value, inputs the modified estimation data into the pre-trained deep learning regression model, and recalculate an optical signal reception power, for controlling the transmitter of the transmission network equipment.

4 . The apparatus of claim 1 , wherein the pre-trained deep learning regression model is a deep learning model with one or more hidden layers.

5 . The apparatus of claim 1 , wherein the processor:

generate at least one train data set for optical signal transmission power and optical signal reception power based on at least one feature value and label value; and

training the pre-trained deep learning regression model using the generated at least one train data set.

6 . The apparatus of claim 5 , wherein the feature vectors include optical signal transmission power, optical cable length, number of patch sections, number of attenuators, and equipment type.

7 . The apparatus of claim 5 , wherein the label value includes optical signal reception power and optical signal transmission power.

8 . A method of estimating and controlling an optical signal transmission power and transmission network facility information, the method comprising:

generating estimation data including an estimated optical signal transmission power and transmission network facility information;

inputting the estimation data into a pre-trained deep learning regression model;

calculating an estimated optical signal reception power based on the estimation data;

determining an optimal optical signal transmission power based on whether the calculated optical signal reception power satisfies a predetermined reception power condition; and

controlling a transmitter of the transmission network equipment to transmit an optical signal at the determined optimal optical signal transmission power.

9 . The method of claim 8 , further comprising: determining the estimated optical signal transmission power as the optimal optical signal transmission power in an event that the calculated optical signal reception power satisfies a predetermined value.

10 . The method of claim 8 , further comprising:

modifying the estimation data in an event that the calculated optical signal reception power does not satisfy the predetermined value;

inputting the modified estimation data into the pre-trained deep learning regression model; and

recalculating an optical signal reception power, for controlling the transmitter of the transmission network equipment.

11 . The method of claim 8 , wherein the pre-trained deep learning regression model is a deep learning model with one or more hidden layers.

12 . The method of claim 8 , further comprising:

generating at least one train data set for optical signal transmission power and optical signal reception power based on at least one feature value and label value; and

training the pre-trained deep learning regression model using the generated at least one train data set.

13 . The method of claim 12 , wherein the feature vectors include optical signal transmission power, optical cable length, number of patch sections, number of attenuators, and equipment type.

14 . The method of claim 12 , wherein the label value includes optical signal reception power and optical signal transmission power.

15 . A non-transitory computer-readable medium storing computer-readable instructions such that, when executed by a processor, cause the processor to perform a method of estimating and controlling an optimal optical signal transmission power for transmission network equipment, the method comprising:

generating estimation data including an estimated optical signal transmission power and transmission network facility information;

inputting the estimation data into a pre-trained deep learning regression model;

calculating an estimated optical signal reception power based on whether the calculated optical signal reception power satisfies a predetermined reception power condition; and

controlling a transmitter of the transmission network equipment to output an optical signal at the determined optimal optical signal transmission power.