Abstract: A dynamic time-division duplexing (D-TDD) control method and system for a UAV-assisted 6G small-cell network includes: collecting geographic information and channel states for base stations (BSs) and user equipment (UEs) within a network coverage area and converting the collected data into a traffic demand density matrix; applying the traffic demand density matrix to a sparse convolutional neural network (CNN) model to extract a feature vector; defining the extracted feature vector as a current state, inputting the current state into a reinforcement learning (RL) model, and determining, by the RL model based on the current state, an action for reconfiguring a slot configuration of a BS; and, in response to the determined action, reconfiguring the slot configuration of the BS and computing a state transition and a reward value.

Abstract: A D2D scheduling method in a UAV-based IoT network includes: (a) acquiring a geographical map for all transmission links of a D2D network within a network coverage area; (b) applying the geographical map to a sparse convolution model to extract a feature map; (c) defining the feature map for a time slot t as a state St, and then inputting the feature map to actor and critic networks of a reinforcement learning-based scheduling policy learning model, respectively, and selecting a scheduling decision At for the D2D transmission link based on a scheduling output of the actor network and a greedy strategy; and (d) transmitting the scheduling decision At to the D2D network and then receiving reward when the scheduling decision At is applied by the D2D network, wherein the reward is calculated as a total achievable transmission rate.