Traffic balancing for moving users, proactive slice management, and predictive slice management using artificial intelligence

1 . One or more computing systems, comprising:

memory storing computer program instructions; and

at least one processor configured to execute the computer program instructions, wherein the computer program instructions are configured to cause the at least one processor to:

receive traffic information from one or more base stations of a Radio Access Network (RAN),

determine, by a near-real time RAN Intelligent Controller (RT RIC) that uses one or more Artificial Intelligence (AI)/Machine Learning (ML) models, based on the received traffic information that one or more network slices for a mobile device should be configured,

send, by the RT RIC, control instructions to at least one base station of the one or more base stations to configure the one or more network slices for the mobile device;

store the traffic information, user profile information, signal information, and spectrum information for the RAN in a data lake;

train, by a non-real time (NRT) RIC or another application of a network core, the one or more AI/ML models using the stored traffic information, the user profile information, the signal information, and the spectrum information from the data lake; and

deploy, by the NRT RIC, the one or more trained AI/MVL models to the RT RIC via an AI interface.

2 . The one or more computing systems of claim 1 , wherein the computer program instructions are further configured to cause the at least one processor to:

receive, by the RT RIC, respective signal information from a plurality of mobile devices that use the RAN; and

use, by the RT RIC and the trained one or more AI/ML models, the received respective signal information in addition to the received traffic information for determining that the that one or more network slices for a mobile device should be configured.

3 . The one or more computing systems of claim 1 , wherein

the receiving of the traffic information, the determining that the one or more network slices for the mobile device should be modified, and the sending of the control instructions are performed by an xApp of the RT RIC.

4 . The one or more computing systems of claim 1 , wherein the control instructions are based on a profile of a user of the mobile device and expected data usage for the profile.

5 . The one or more computing systems of claim 1 , wherein the one or more network slices are configured to provide an amount of data that the RT RIC anticipates one or more applications running on the mobile device will use.

6 . The one or more computing systems of claim 1 , wherein the configuring of the one or more network slices comprises at least one of assigning bit rates, assigning one or more bands, assigning one or more cell sites, and setting up Carrier Aggregation (CA) for the mobile device.

7 . The one or more computing systems of claim 1 , wherein the one or more network slices provide at least one of guaranteed downlink throughput, guaranteed uplink throughput, and guaranteed maximum latency.

8 . The one or more computing systems of claim 1 , wherein the RIC is a near-real time (NT) RIC in the RAN and the computer program instructions are further configured to cause the at least one processor to:

continue to collect and store the received traffic information, user profile information, signal information, and spectrum information for the RAN in the data lake;

retrain, by the non-real time (NRT) RIC or another application of the network core, the one or more AI/IL models and/or train one or more new AI/ML models using the stored received traffic information, the user profile information, the signal information, and the spectrum information from the data lake; and

deploy, by the NRT RIC, the one or more trained AI/NL models and/or the one or more trained new AI/NL models to the RT RIC via the AI interface.

9 . A computer-implemented method, comprising:

monitoring, by a near-real time Radio Access Network (RAN) Intelligent Controller (RT RIC), network availability information and load information from a RAN;

determining, by the RT RIC using one or more Artificial Intelligence (AI)/Machine Learning (ML) models, that proactive load balancing should be performed for the RAN upon one or more User Equipment (UE) devices entering a coverage area; and

sending, by the RIC, control instructions to one or more base stations of the RAN to perform the proactive load balancing; and

storing the traffic information, user profile information, signal information, and spectrum information for the RAN in a data lake;

training, by a non-real time (NRT) RIC or another application of a network core, the one or more AI/NL models using the stored traffic information, the user profile information, the signal information, and the spectrum information from the data lake; and

deploying, by the NRT RIC, the one or more trained AI/ML models to the RT RIC via an AI interface.

10 . The computer-implemented method of claim 9 , the method further comprises:

retrieving, by the non-real time (NRT) RIC, the signal information, the load information, the user profile information, and the spectrum information from the data lake;

training, by the NRT RIC, the one or more AI/MVL models using the signal information, the load information, the user profile information, and the spectrum information from the data lake; and

deploying, by the NRT RIC, the one or more trained AI/ML models to the RT RIC.

11 . The computer-implemented method of claim 9 , wherein the determining that the proactive load balancing should be performed comprises at least one of determining overall RAN usage, individual cell site loads, and power consumption.

12 . The computer-implemented method of claim 9 , further comprising:

determining, by the RT RIC, that a User Equipment (UE) device of the one or more user devices has moved into a different coverage area in the RAN; and

performing, by the RAN, the proactive load balancing responsive to the determination.

13 . The computer-implemented method of claim 9 , wherein the proactive load balancing is performed responsive to a predetermined number of User Equipment (UE) devices moving into the coverage area of the RAN.

14 . The computer-implemented method of claim 9 , wherein the proactive load balancing comprises moving one or more User Equipment (UE) devices of the one or more UE devices to one or more cell sites based on current load conditions of the RAN.

15 . The computer-implemented method of claim 9 , wherein the proactive load balancing comprises moving one or more User Equipment (UE) devices to one or more cell sites to reduce power consumption in the RAN.

16 . One or more computing systems, comprising:

memory storing computer program instructions; and

at least one processor configured to execute the stored computer program instructions, wherein the computer program instructions are configured to cause the at least one processor to:

monitor, by a near-real time Radio Access Network (RAN) Intelligent Controller (RT RIC), a RAN using one or more Artificial Intelligence (AI)/Machine Learning (ML) models,

determine, by the RT RIC using the one or more AI/ML models, that predictive slice management should be performed based on predicted application usage on a User Equipment (UE) device,

configure and allocate, by the RT RIC, one or more network slices to the UE device based on the predicted application usage;

retrieve, by a non-real time (NRT) RIC, signal information, traffic information, network load information, user profile information, and spectrum information from a data lake;

train, by the NRT RIC, the one or more AI/ML models using the signal information, the traffic information, the network load information, the user profile information, and the spectrum information retrieved from the data lake; and

deploy, by the NRT RIC, the trained one or more AI/ML models to the RT RIC.

17 . The one or more computing systems of claim 16 , wherein the trained one or more AI/ML models are configured to determine which application is being used by the UE device based on at least one of an application identifier (App ID), Deep Packet Inspection (DPI), one or more Internet Protocol (IP) addresses, and one or more ports.

18 . The one or more computing systems of claim 16 , wherein

the one or more network slices are allocated on the fly for a period of time by sending control information to one or more base stations of the RAN, and

the allocation of the one or more network slices comprises at least one of moving one or more other UE devices to a different cell site, moving the one or more other UE devices to a different frequency band, and reducing data rates of the one or more other UE devices when a user of the UE device to be allocated to the one or more network slices has a higher quality subscription.