Apparatuses and methods for faciliating an identification and scheduling of resources for reduced capability devices

A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:

• obtaining data pertaining to a first user equipment and a first network resource;
• analyzing the data to:
• identify that the first user equipment is a reduced capability device, identify a first location of the first user equipment, identify that the first user equipment is executing a first application, predict that the first user equipment will execute a second application based on at least one machine learning model, identify a first communication session that is supported by the first network resource, predict, using the at least one machine learning model, a second communication session that is to be supported by the first network resource and a second network resource that is different from the first network resource, or any combination thereof, wherein the first application and the second application are different from one another;
• based on the analyzing, allocating the first network resource and a third network resource to the first user equipment, resulting in a first allocation;
• and
• based on the analyzing, adjusting parameters of the first user equipment, wherein the parameters pertain to:
• a transmission power level and a receiver sensitivity level.

A method comprising:

• obtaining, by a processing system including a processor, data;
• analyzing, by the processing system, the data to:
• identify that a first user equipment is a reduced capability device, identify a first location of the first user equipment relative to a second location of a second user equipment, identify that the first user equipment is executing a first application, predict that the second user equipment will execute a second application based on at least one machine learning model, identify first communication sessions that are supported by first network resources, predict, using the at least one machine learning model, second communication sessions that are to be supported by the first network resources and third communication sessions that are to be supported by second network resources, or any combination thereof, the second network resources being at least partially different from the first network resources;
• based on the analyzing, allocating, by the processing system, the first network resources or the second network resources to the first user equipment, resulting in a first allocation;
• and
• based on the analyzing, adjusting a first plurality of communication parameters of the first user equipment, wherein the first plurality of communication parameters pertains to:
• a transmission power level and a receiver sensitivity level.