Systems and methods for machine learning based access class barring
Systems and methods for ML based ACB are provided herein. In an example, a system includes BBU(s), RU(s) communicatively coupled to the BBU(s), and antenna(s) communicatively coupled to the RU(s). Each respective RU of the RU(s) is communicatively coupled to a respective subset of the antenna(s). The BBU(s), the RU(s), and the antenna(s) are configured to implement a base station for wirelessly communicating with UEs in a cell. The system includes a machine learning computing system configured to: receive time data, traffic data, and location data; and determine predicted barring parameter(s) for the base station based on the time data, the traffic data, and the location data. The system is configured to: adjust barring factor(s) and/or barring time(s) in an information message based on the predicted barring parameter(s) for the base station; and send the information message with the adjusted barring factor(s) and/or barring time(s) to UEs in the cell.
1 . A system, comprising:
at least one baseband unit (BBU) entity;
one or more radio units communicatively coupled to the at least one BBU entity;
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 entity, 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 in a cell; and
a machine learning computing system configured to:
receive time data, traffic data, and location data; and
predict one or more predicted barring parameters for the base station based on the time data, the traffic data, and the location data;
wherein the system is configured to:
adjust at least one barring factor or barring time in an information message based on the one or more predicted barring parameters for the base station to generate adjusted at least one barring factor or barring time; and
send the information message with the adjusted at least one barring factor or barring time to user equipment in the cell.
2 . The system of claim 1 , wherein the time data, the traffic data, and the location data include:
a time of day;
a day of week;
an indicator of whether it is a holiday;
a load factor;
a congestion factor; and
a type of location when the base station resides.
3 . The system of claim 1 , wherein the machine learning computing system is configured to predict a single predicted barring parameter for the base station that is applicable to multiple access classes or identities of user equipment in the cell or multiple traffic types or categories.
4 . The system of claim 1 , wherein the machine learning computing system is configured to:
predict a first predicted barring parameter for the base station that is applicable to a first access class or identity of user equipment in the cell or a first traffic type or category; and
predict a second predicted barring parameter for the base station that is applicable to a second access class or identity of user equipment in the cell or a second traffic type or category.
5 . The system of claim 4 , wherein the first access class or identity of user equipment in the cell or the second access class or identity of user equipment in the cell includes high-priority users.
6 . The system of claim 4 , wherein the first traffic type or category or the second traffic type or category includes high-priority traffic or emergency traffic.
7 . The system of claim 1 , wherein the one or more predicted barring parameters include one or more predicted barring factors.
8 . The system of claim 1 , wherein the one or more predicted barring parameters include one or more predicted barring times.
9 . The system of claim 1 , wherein the one or more predicted barring parameters include one or more predicted barring factors and one or more predicted barring times.
10 . 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.
11 . The system of claim 1 , wherein the at least one BBU entity includes a central unit communicatively coupled to a distributed unit, wherein the distributed unit is communicatively coupled to the one or more radio units, wherein the machine learning computing system is implemented in a radio access network intelligent controller.
12 . A method, comprising:
receiving time data, traffic data, and location data;
predicting one or more predicted barring parameters for a base station based on the time data, the traffic data, and the location data, wherein at least one baseband unit (BBU) entity, one or more radio units, and one or more antennas are configured to implement the base station for wirelessly communicating with user equipment in a cell, wherein the one or more radio units communicatively coupled to the at least one BBU entity, wherein the one or more antennas are 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;
adjusting at least one barring factor or barring time in an information message based on the one or more predicted barring parameters for the base station to generate adjusted at least one barring factor or barring time; and
sending the information message with the adjusted at least one or more barring factor or barring time to user equipment in the cell.
13 . The method of claim 12 , wherein the time data, the traffic data, and the location data include:
a time of day;
a day of week;
an indicator of whether it is a holiday;
a load factor;
a congestion factor; and
a type of location when the base station resides.
14 . The method of claim 12 , wherein predicting one or more predicted barring parameters for a base station based on the time data, the traffic data, and the location data includes predicting a single predicted barring parameter for the base station that is applicable to multiple access classes or identities of user equipment in the cell or multiple traffic types or categories.
15 . The method of claim 12 , wherein predicting one or more predicted barring parameters for a base station based on the time data, the traffic data, and the location data includes:
predicting a first predicted barring parameter for the base station that is applicable to first access class or identity of user equipment in the cell or a first traffic type or category; and
predicting a second predicted barring parameter for the base station that is applicable to a second access class or identity of user equipment in the cell or a second traffic type or category.
16 . The method of claim 15 , wherein the first access class or identity of user equipment in the cell or the second access class or identity of user equipment in the cell includes high-priority users.
17 . The method of claim 15 , wherein the first traffic type or category or the second traffic type or category includes high-priority traffic or emergency traffic.
18 . The method of claim 12 , wherein the one or more predicted barring parameters include one or more predicted barring factors.
19 . The method of claim 12 , wherein the one or more predicted barring parameters include one or more predicted barring times.
20 . The method of claim 12 , wherein the one or more predicted barring parameters include one or more predicted barring factors and one or more predicted barring times.