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In this paper, the problem of trajectory design of unmanned aerial vehicles(UAVs) for maximizing the number of satisfied users is studied in a UAV basedcellular network where the UAV works as a flying base station that servesusers, and the user indicates its satisfaction in terms of completion of itsdata request within an allowable maximum waiting time. The trajectory design isformulated as an optimization problem whose goal is to maximize the number ofsatisfied users. To solve this problem, a machine learning framework based ondouble Q-learning algorithm is proposed. The algorithm enables the UAV to findthe optimal trajectory that maximizes the number of satisfied users. Comparedto the traditional learning algorithms, such as Q-learning that selects andevaluates the action using the same Q-table, the proposed algorithm candecouple the selection from the evaluation, therefore avoid overestimationwhich leads to sub-optimal policies. Simulation results show that the proposedalgorithm can achieve up to 19.4% and 14.1% gains in terms of the number ofsatisfied users compared to random algorithm and Q-learning algorithm.

Optimized Trajectory Design in UAV Based Cellular Networks for 3D Users: A Double Q-Learning Approach