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

Abstract

In this paper, 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 on-line computations. In this work, two relevant sub-problems are decoupled from the joint UDUA problem: the first one is denoted as the user association sub-problem, for certain UAV base station (UAV-BS) positions, this sub-problem is settled for finding the strategy which matches aerial and ground nodes optimally. The second sub-problem is the positioning of UAV-BSs in order to obtain the best possible solution to the user association sub-problem from all possible positioning combinations for the UAV-BSs. In the proposed mechanism, the Kuhn-Munkres algorithm is used to solve the first sub-problem as an equivalent bipartite matching problem. For the UAV-BS deployment sub-problem, 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 sub-problem. 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.