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

In this article, a time-efficient mechanism is proposed to solve the joint unmanned aerial vehicle (UAV) base station deployment and user/sensor association (UDUA) problem aiming at maximizing the downlink sum transmission throughput and reducing the time of online computations. In this work, two relevant subproblems are decoupled from the joint UDUA problem: the first one is denoted as the user association subproblem, for certain UAV base station (UAV-BS) positions, this subproblem is settled for finding the strategy which matches aerial and ground nodes optimally. The second subproblem is the positioning of UAV-BSs in order to obtain the best possible solution to the user association subproblem from all possible positioning combinations for the UAV-BSs. In the proposed mechanism, the Kuhn-Munkres algorithm is used to solve the first subproblem as an equivalent bipartite matching problem. For the UAV-BS deployment subproblem, when the two user distributions own a high similarity, we theoretically prove that little performance decline will be introduced when the new user distribution's optimal strategy is compared with choosing the optimal UAV-BS deployment strategy of stored user distributions. Based on our mathematical analyses, the similarity level between user distributions is well defined and becomes the key to solve the second subproblem. According to experimental findings, the proposed UDUA mechanism, when compared to benchmark approaches, can provide near-optimum system performance with regard to average downlink total transmission throughput and failure rate with significantly decreased computing time.