AI for scheduling: reinforcement learning for radio resource allocation

Limitations of Traditional Schedulers

Traditional RAN schedulers use fixed algorithms — proportional fair, round robin, or maximum throughput — with static parameters tuned during deployment. These schedulers cannot adapt to dynamic traffic patterns, varying channel conditions, or mixed service requirements simultaneously. A proportional fair scheduler treats a video stream and a sensor update identically. When traffic composition shifts from peak-hour video to late-night IoT, the scheduler applies the same resource allocation logic. This rigidity leaves significant throughput on the table and fails to meet differentiated QoS…