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This paper proposes a time-efficient mechanism to decrease the on-linecomputing time of solving the joint unmanned aerial vehicle base station(UAV-BS) deployment and user/sensor association (UDUA) problem aiming atmaximizing the downlink sum transmission throughput. The joint UDUA problem isdecoupled into two sub-problems: one is the user association sub-problem, whichgets the optimal matching strategy between aerial and ground nodes for certainUAV-BS positions; and the other is the UAV-BS deployment sub-problem trying tofind the best position combination of the UAV-BSs that make the solution of thefirst sub-problem optimal among all the possible position combinations of theUAV-BSs. In the proposed mechanism, we transform the user associationsub-problem into an equivalent bipartite matching problem and solve it usingthe Kuhn-Munkres algorithm. For the UAV-BS deployment sub-problem, wetheoretically prove that adopting the best UAV-BS deployment strategy of aprevious user distribution for each new user distribution will introduce littleperformance decline compared with the new user distribution's ground true beststrategy if the two user distributions are similar enough. Based on ourmathematical analyses, the similarity level between user distributions is welldefined and becomes the key to solve the second sub-problem. Numerical resultsindicate that the proposed UDUA mechanism can achieve near-optimal systemperformance in terms of average downlink sum transmission throughput andfailure rate with enormously reduced computing time compared with benchmarkapproaches.

Time Efficient Joint UAV-BS Deployment and User Association based on Machine Learning