Method for creating network simulation platform, network simulation method, and corresponding apparatuses
A method for creating a network simulation platform that has a first black box model, includes: obtaining a candidate network performance indicator and feature information related to the candidate network performance indicator, where the feature information related to the candidate network performance indicator refers to a part or all of feature information needed to obtain the candidate network performance indicator; and training a machine learning model to obtain the first black box model, where an output parameter of the first black box model is the candidate network performance indicator, input parameters of the first black box model are the feature information related to the candidate network performance indicator, and the first black box model is used to simulate the candidate network performance indicator. The solutions provided help reduce simulation complexity and are applicable to large-scale network simulation.
1. A method for creating a network simulation platform, wherein the network simulation platform comprises at least one black box model, and the at least one black box model comprises a first black box model, the method comprising:
obtaining a candidate network performance indicator and feature information related to the candidate network performance indicator, wherein the feature information related to the candidate network performance indicator refers to a part or all of feature information needed to obtain the candidate network performance indicator; and
training a machine learning model to obtain the first black box model, wherein an output parameter of the first black box model is the candidate network performance indicator, input parameters of the first black box model are the feature information related to the candidate network performance indicator, and the first black box model is used to simulate the candidate network performance indicator.
2. The method according to claim 1 , wherein the candidate network performance indicator comprises: a network performance indicator in a core network, a network performance indicator in a bearing network, a network performance indicator in an access network, or a cross-network network performance indicator.
3. The method according to claim 1 , wherein the at least one black box model further comprises a second black box model, and an output parameter of the second black box model is one of the input parameters of the first black box model.
4. The method according to claim 3 , wherein the output parameter of the first black box model is a network performance indicator in a first network, and the output parameter of the second black box model is a network performance indicator in a second network; wherein
the first network is a core network, a bearing network, or an access network; and the second network is any network other than the first network among the core network, the bearing network, and the access network.
5. The method according to claim 1 , further comprising:
obtaining training data, wherein the training data comes from an actual network or another simulation platform other than the simulation platform; and
the training of the machine learning model to obtain the first black box model comprises:
training the machine learning model based on the training data to obtain the first black box model.
6. The method according to claim 1 , wherein the machine learning model comprises a neural network model.
7. The method according to claim 1 , wherein
the candidate network performance indicator comprises a rank, and feature information related to the rank comprises a channel matrix; or
the candidate network performance indicator comprises a channel matrix, and feature information related to the channel matrix comprises a multi-path parameter and an antenna configuration parameter; or
the candidate network performance indicator comprises a multi-path parameter, and feature information related to the multi-path parameter comprises an electronic map, an engineering parameter, and a clutter type; or
the candidate network performance indicator comprises a channel state probability distribution parameter of a cell, and feature information related to the channel state probability distribution parameter of the cell comprises a grid channel matrix of the cell and a channel state holding time sequence of the cell; or
the candidate network performance indicator comprises a precoding matrix of a cell, and feature information related to the precoding matrix of the cell comprises a grid channel matrix of the cell.
8. A network simulation method, wherein the method is applied to a network simulation platform that comprises at least two black box models, the method comprising:
determining a first to-be-simulated network performance indicator;
searching, from the at least two black box models, for a first black box model used to simulate the first to-be-simulated network performance indicator, wherein an output parameter of the first black model is the first to-be-simulated network performance indicator; and
inputting values of input parameters of the first black box model into the first black box model to obtain a value of the output parameter of the first black box model, and using the obtained value as a simulation result of the first to-be-simulated network performance indicator.
9. The method according to claim 8 , wherein the first to-be-simulated network performance indicator comprises: a network performance indicator in a core network, a network performance indicator in a bearing network, a network performance indicator in an access network, or a cross-network network performance indicator.
10. The method according to claim 8 , further comprising:
searching, from the at least two black box models, for a second black box model used to simulate a second to-be-simulated network performance indicator, wherein an output parameter of the second black box model is one of the input parameters of the first black box model; and
inputting values of input parameters of the second black box model into the second black box model to obtain a value of the output parameter of the second black box model, and using the obtained value as a simulation result of the second to-be-simulated network performance indicator; and
the inputting values of the input parameters of the first black box model into the first black box model comprises:
inputting the simulation result of the second to-be-simulated network performance indicator into the first black box model.
11. The method according to claim 10 , wherein the output parameter of the first black box model is a network performance indicator in a first network, and the output parameter of the second black box model is a network performance indicator in a second network; wherein
the first network is a core network, a bearing network, or an access network; and the second network is any network other than the first network among the core network, the bearing network, and the access network.
12. The method according to claim 8 , wherein
the first to-be-simulated network performance indicator comprises a rank, and feature information related to the rank comprises a channel matrix; or
the first to-be-simulated network performance indicator comprises a channel matrix, and the output parameter of the first black box model comprises a multi-path parameter and an antenna configuration parameter; or
the first to-be-simulated network performance indicator comprises a multi-path parameter, and the output parameter of the first black box model comprises an electronic map, an engineering parameter, and a clutter type; or
the first to-be-simulated network performance indicator comprises a channel state probability distribution parameter of a cell, and the output parameter of the first black box model comprises a grid channel matrix of the cell and a channel state holding time sequence of the cell; or
the first to-be-simulated network performance indicator comprises a precoding matrix of a cell, and the output parameter of the first black box model comprises a grid channel matrix of the cell.
13. A network simulation system, comprising an apparatus for creating a network simulation platform and a network simulation apparatus, wherein the network simulation platform comprises at least one black box model, and the at least one black box model comprises a first black box model, and
the apparatus for creating a network simulation platform comprises at least one first processor and a first memory, wherein the first memory is configured to store a first program, and wherein the first program, when executed by the at least one first processor, causes the apparatus for creating a network simulation platform to:
obtain a candidate network performance indicator and feature information related to the candidate network performance indicator, wherein the feature information related to the candidate network performance indicator refers to a part or all of feature information needed to obtain the candidate network performance indicator; and
train a machine learning model to obtain the first black box model, wherein an output parameter of the first black box model is the candidate network performance indicator, input parameters of the first black box model are the feature information related to the candidate network performance indicator, and the first black box model is used to simulate the candidate network performance indicator;
and the network simulation apparatus comprises at least one second processor and a second memory, wherein the second memory is configured to store a program, and wherein the second program, when executed by the at least one second processor, causes the network simulation apparatus to:
determine a first to-be-simulated network performance indicator;
search, from the at least two black box models, for a first black box model used to simulate the first to-be-simulated network performance indicator, wherein an output parameter of the first black model is the first to-be-simulated network performance indicator; and
input values of input parameters of the first black box model into the first black box model to obtain a value of the output parameter of the first black box model, and using the obtained value as a simulation result of the first to-be-simulated network performance indicator.
14. The network simulation system according to claim 13 , wherein the candidate network performance indicator comprises: a network performance indicator in a core network, a network performance indicator in a bearing network, a network performance indicator in an access network, or a cross-network network performance indicator.
15. The network simulation system according to claim 13 , wherein the at least one black box model further comprises a second black box model, and an output parameter of the second black box model is one of the input parameters of the first black box model.
16. The network simulation system according to claim 13 , wherein the first program, when executed by the at least one first processor, further causes the apparatus for creating a network simulation platform to:
obtain training data, wherein the training data comes from an actual network or another simulation platform other than the simulation platform; and
train the machine learning model based on the training data to obtain the first black box model.
17. The network simulation system according to claim 13 , wherein the machine learning model comprises a neural network model.
18. The network simulation system according to claim 13 , wherein the second program, when executed by the at least one second processor, further causes the network simulation apparatus to:
search, from the at least two black box models, for a second black box model used to simulate a second to-be-simulated network performance indicator, wherein an output parameter of the second black box model is one of the input parameters of the first black box model; and
input values of input parameters of the second black box model into the second black box model to obtain a value of the output parameter of the second black box model, and use the obtained value as a simulation result of the second to-be-simulated network performance indicator; and
input the simulation result of the second to-be-simulated network performance indicator into the first black box model.
19. The network simulation system according to claim 13 , wherein the output parameter of the first black box model is a network performance indicator in a first network, and the output parameter of the second black box model is a network performance indicator in a second network; wherein
the first network is a core network, a bearing network, or an access network; and the second network is any network other than the first network among the core network, the bearing network, and the access network.
20. The network simulation system according to claim 13 , wherein
the first to-be-simulated network performance indicator comprises a rank, and feature information related to the rank comprises a channel matrix; or
the first to-be-simulated network performance indicator comprises a channel matrix, and the output parameter of the first black box model comprises a multi-path parameter and an antenna configuration parameter; or
the first to-be-simulated network performance indicator comprises a multi-path parameter, and the output parameter of the first black box model comprises an electronic map, an engineering parameter, and a clutter type; or
the first to-be-simulated network performance indicator comprises a channel state probability distribution parameter of a cell, and the output parameter of the first black box model comprises a grid channel matrix of the cell and a channel state holding time sequence of the cell; or
the first to-be-simulated network performance indicator comprises a precoding matrix of a cell, and the output parameter of the first black box model comprises a grid channel matrix of the cell.