SYSTEMS AND METHODS FOR MACHINE LEARNING BASED SLICE MODIFICATION, ADDITION, AND DELETION
Systems and methods for machine learning based network slice modification, addition, and deletion are provided. In one example, a method includes receiving time data, traffic data, and QoS data and determining a predicted radio resource usage of a base station based on the time data, traffic data, and QoS data. The base station includes at least one BBU, radio unit(s) communicatively coupled to the at least one BBU, and antenna(s) communicatively coupled to the radio unit(s). Each respective radio unit is communicatively coupled to a respective subset of the antenna(s). The at least one BBU, the radio unit(s), and the antenna(s) are configured to implement a base station for wirelessly communicating with user equipment. The method further includes dynamically modifying, adding, or deleting one or more network slices based on the predicted radio resource usage of the base station.
1 . A system, comprising:
at least one baseband unit (BBU);
one or more radio units communicatively coupled to the at least one BBU;
one or more antennas communicatively coupled to the one or more radio units, wherein each respective radio unit of the one or more radio units is communicatively coupled to a respective subset of the one or more antennas;
wherein the at least one BBU, the one or more radio units, and the one or more antennas are configured to implement a base station for wirelessly communicating with user equipment; and
a machine learning computing system configured to:
receive time data, traffic data, and quality of service (QoS) data; and
determine a predicted radio resource usage of the base station based on the time data, the traffic data, and the QoS data;
wherein the system is configured to dynamically modify, add, or delete a network slice based on the predicted radio resource usage of the base station.
2 . The system of claim 1 , wherein the time data, the traffic data, and the QoS data includes:
time of day;
day of week;
a number of user equipment wirelessly communicating with the base station; and
active quality of service identifiers.
3 . (canceled)
4 . The system of claim 1 , wherein the system is configured to dynamically add or delete a network slice based on the predicted radio resource usage of the base station.
5 . (canceled)
6 . The system of claim 1 , wherein the system is configured to dynamically modify a network slice based on the predicted radio resource usage of the base station.
7 . The system of claim 1 , wherein the network slice includes a share of transport resources, core network resources, and radio access network resources.
8 . The system of claim 1 , wherein the machine learning computing system is configured to utilize the time data, the traffic data, and the QoS data as inputs to a plurality of machine learning models, wherein each machine learning model of the plurality of machine learning models is directed to a respective one or more quality of service identifiers, a respective frequency band, and/or a respective operator.
9 . (canceled)
10 . (canceled)
11 . The system of claim 1 , wherein the one or more radio units includes a plurality of radio units, wherein the one or more antennas includes a plurality of antennas.
12 . (canceled)
13 . (canceled)
14 . A method, comprising:
receiving time data, traffic data, and quality of service (QoS) data;
determining a predicted radio resource usage of a base station based on the time data, the traffic data, and the QoS data, wherein the base station includes at least one baseband unit (BBU), one or more radio units communicatively coupled to the at least one BBU, and one or more antennas communicatively coupled to the one or more radio units, wherein each respective radio unit of the one or more radio units is communicatively coupled to a respective subset of the one or more antennas, wherein the at least one BBU, the one or more radio units, and the one or more antennas are configured to implement a base station for wirelessly communicating with user equipment; and
dynamically modifying, adding, or deleting one or more network slices based on the predicted radio resource usage of the base station.
15 . The method of claim 14 , wherein the time data, the traffic data, and the QoS data includes:
time of day;
day of week;
a number of user equipment wirelessly communicating with the base station; and
active quality of service identifiers.
16 . The method of claim 14 , wherein receiving time data and traffic data includes:
receiving at least some of the time data from one or more devices external to the base station;
receiving at least some of the traffic data from one or more devices external to the base station; and/or
receiving at least some of the QoS data from one or more devices external to the base station.
17 . The method of claim 14 , wherein dynamically modifying, adding, or deleting one or more network slices based on the predicted radio resource usage of the base station includes adding or deleting a network slice based on the predicted radio resource usage of the base station.
18 . (canceled)
19 . The method of claim 14 , wherein dynamically modifying, adding, or deleting one or more network slices based on the predicted radio resource usage of the base station includes modifying a network slice based on the predicted radio resource usage of the base station.
20 . (canceled)
21 . The method of claim 14 , wherein determining a predicted radio resource usage of a base station based on the time data, the traffic data, and the QoS data includes utilizing the time data, the traffic data, and the QoS data as inputs to a plurality of machine learning models, wherein each machine learning model of the plurality of machine learning models is directed to a respective one or more quality of service identifiers, a respective frequency band, and/or a respective operator.
22 . (canceled)
23 . (canceled)
24 . A system, comprising:
a distributed antenna system including:
a master unit communicatively coupled to a base station;
one or more remote antenna units communicatively coupled to the master unit, wherein the one or more remote antenna units are located remotely from the master unit, wherein the one or more remote antenna units are configured to communicate wireless signals with user equipment in one or more coverage zones; and
a machine learning computing system configured to:
receive time data, traffic data, and quality of service (QoS) data; and
determine a predicted radio resource usage of the base station based on the time data, the traffic data, and the QoS data;
wherein the system is configured to dynamically modify, add, or delete a network slice based on the predicted radio resource usage of the base station.
25 . The system of claim 24 , wherein the time data, the traffic data, and the QoS data includes:
time of day;
day of week;
a number of user equipment wirelessly communicating with the one or more remote antenna units; and
active quality of service identifiers.
26 . (canceled)
27 . The system of claim 24 , wherein the system is configured to dynamically add or delete a network slice based on the predicted radio resource usage of the base station.
28 . (canceled)
29 . The system of claim 24 , wherein the system is configured to dynamically modify a network slice based on the predicted radio resource usage of the base station.
30 . The system of claim 24 , wherein the network slice includes a share of transport resources, core network resources, and radio access network resources.
31 . The system of claim 24 , wherein the machine learning computing system is configured to utilize the time data, the traffic data, and the QoS data as inputs to a plurality of machine learning models, wherein each machine learning model of the plurality of machine learning models is directed to a respective one or more quality of service identifiers, a respective frequency band, and/or a respective operator.
32 . (canceled)
33 . (canceled)
34 . The system of claim 24 , wherein the one or more remote antenna units includes a plurality of remote antenna units.
35 . (canceled)
36 . (canceled)