TRANSPORT NETWORK DOMAIN SLICING ARCHITECTURE

1 . An apparatus, comprising:

a network slice management function (NSMF) configured to request at least a transport network (TN) domain in a network architecture to create a TN portion of a network slice in a wireless communications system; and

a transport network-network slice subnet management function (TN-NSSMF) configured to manage the TN portion of the network slice;

wherein one of the NSMF and the TN-NSSMF has artificial intelligence/machine learning (AI/ML) integrated therein that is configured to allow the one of the NSMF and the TN-NSSMF to monitor and analyze performance of the network slice in the TN domain.

2 . The apparatus of claim 1 , further comprising a representational state transfer application programming interface (REST-API) interface between the NSMF and the TN-NSSMF.

3 . The apparatus of claim 1 , wherein the one of the NSMF and the TN-NSSMF that has the AI/ML integrated therein is the NSMF.

4 . The apparatus of claim 3 , wherein the NSMF is configured to collect input data from the TN-NSSMF for a workflow of the AI/ML, the input data including one or more of the following:

data mapping between aggregation of radio access network (RAN) and core slices with S-NSSAI and transport slice identifiers (Tx-Slice-ID),

data mapping between Tx-Slice-ID and logical Dedicated Forwarding Planes (DFPs) paths,

telemetry data telemetry data that provides health of a forwarding plane per each DFP,

traffic matrices that provide bandwidth consumption of all the slicing flows for each transport link, and

segment routing over IPv6 (SRv6) performance management (SRv6-PM) reports.

5 . The apparatus of claim 4 , further comprising a representational state transfer application programming interface (REST-API) interface between the NSMF and the TN-NSSMF; and

the NSMF is configured to collect at least some of the input data for the workflow of the AI/ML via the REST-API interface.

6 . The apparatus of claim 4 , wherein the workflow of the AI/ML comprises model training and/or inference.

7 . The apparatus of claim 1 , wherein the one of the NSMF and the TN-NSSMF that has the AI/ML integrated therein is the TN-NSSMF.

8 . The apparatus of claim 7 , wherein the TN-NSSMF includes a network slice controller (NSC) or a TN domain manager.

9 . The apparatus of claim 7 , wherein the TN-NSSMF is configured to collect input data for the AI/ML, the input data including one or more of the following:

data mapping between aggregation of radio access network (RAN) and core slices with S-NSSAI and transport slice identifiers (Tx-Slice-ID),

data mapping between Tx-Slice-ID and logical Dedicated Forwarding Planes (DFPs) paths,

telemetry data telemetry data that provides health of a forwarding plane per each DFP,

traffic matrices that provide bandwidth consumption of all the slicing flows for each transport link, and

segment routing over IPv6 (SRv6) performance management (SRv6-PM) reports.

10 . The apparatus of claim 7 , further comprising a representational state transfer application programming interface (REST-API) interface between the NSMF and the TN-NSSMF, wherein the TN-NSSMF is configured to collect slice-mapping and application service level agreement (SLA) information from the NSMF via the REST-API interface.

11 . The apparatus of claim 1 , wherein the NSMF is configured to be communicatively coupled to the TN domain, a radio access network (RAN) domain, and a core network (CN) domain.

12 . The apparatus of claim 11 , wherein the RAN domain includes at least one base station therein, the base station including at least one of an eNodeB and a gNodeB.

13 . The apparatus of claim 1 , wherein the wireless communications system includes at least one of a 5G New Radio (NR) communications system and a long term evolution (LTE) communication system.

14 . The apparatus of claim 1 , wherein the AI/ML comprises a linear regression model, a Feed Forward Network (FFN)/Convolutional Neural Network (CNN) model, or a Long Short-Term Memory (LSTM) model).

15 . The apparatus of claim 14 , wherein the AI/ML comprises a model repository including one or more of the linear regression model, the FFN/CNN model, and the LSTM model.

16 . The apparatus of claim 15 , wherein the AI/ML is configured to select a model from the model repository at random or based on initial configuration requirements input by a user.

17 . The apparatus of claim 16 , wherein the AI/ML is configured to use the selected model to perform an evaluation of structured data packets and/or configuration parameters of the wireless communications system to generate a performance score of the network slice.

18 . The apparatus of claim 17 , wherein the configuration parameters comprises one or more of: TN topology information, TN configuration information, high-level policy information, and subnet information.

19 . The apparatus of claim 17 , wherein the AI/ML is configured to take a corrective action based on the performance score of the network slice, and wherein the corrective action comprises creation of a new forwarding plane or assigning additional networks to the network slice.

20 . An apparatus, comprising:

a network slice management function (NSMF) including:

at least one first processor, and

at least one first non-transitory storage media storing instructions that, when executed by the at least one first processor, request at least a transport network (TN) domain in a network architecture to create a TN portion of a network slice in a wireless communications system; and

a transport network-network slice subnet management function (TN-NSSMF) including:

at least one second processor, and

at least one second non-transitory storage media storing instructions that, when executed by the at least one second processor, manage the TN portion of the network slice;

wherein one of the NSMF and the TN-NSSMF has artificial intelligence/machine learning (AI/ML) integrated therein that is configured to allow the one of the NSMF and the TN-NSSMF to monitor and analyze performance of the network slice in the TN domain.

21 - 38 . (canceled)