Method of scheduling plurality of packets related to tasks of plurality of user equipments using artificial intelligence and electronic device performing the method

An electronic device configured to schedule a plurality of packets to allocate radio resources to a plurality of packets related to tasks of a plurality of user equipments (UEs) and stacked in a queue, the electronic device comprising:

• at least one processor;
• and
• a memory connected to the at least one processor and storing a scheduling parameter,
• wherein the at least one processor is configured to:
• set priorities for a plurality of packets stacked in the queue at a first point in time based on a preset scheduling parameter;
• allocate radio resources to at least some of the plurality of packets for which the priorities are set based on the set priorities;
• obtain a scheduling parameter updated using at least one artificial intelligence (AI) learning model;
• and
• set the priorities for the plurality of packets stacked in the queue at a second point in time later than the first point in time, based on the updated scheduling parameter.

A system configured to schedule a plurality of packets to allocate radio resources to a plurality of packets related to tasks of a plurality of user equipments (UEs) and stacked in a queue, the system comprising:

• an artificial intelligence (AI) module comprising at least one AI learning model and a data processor;
• wherein
• the data processor is configured to:
• set priorities for a plurality of packets stacked in the queue at a first point in time based on a preset scheduling parameter, allocate radio resources to at least some of the plurality of packets for which the priorities are set based on the set priorities, obtain a scheduling parameter updated using at least one artificial intelligence (AI) learning model from the AI module, and set the priorities for the plurality of packets stacked in the queue at a second point in time later than the first point in time, based on the updated scheduling parameter,
• wherein the AI module is configured to:
• obtain, from the data processor, packet processing information as a result of allocating the radio resources to the at least some of the plurality of packets for which the priorities are set;
• generate the updated scheduling parameter based on the obtained packet processing information, using the at least one AI learning model;
• and
• deliver the updated scheduling parameter to the data processor.