Beyond Empirical Models: Pattern Formation Driven Placement of UAV Base Stations

Abstract

This work considers the placement of unmanned aerial vehicle base stations (UAV-BSs) with criterion of minimum UAV-recall-frequency (UAV-RF), indicating the energy efficiency of mobile UAVs networks. Several different power consumptions, including signal transmit power, on-board circuit power and the power for UAVs mobility, and the ground user density are taken into account. Instead of conventional empirical stochastic models, this paper utilizes a pattern formation system to track the instable and non-ergodic time-varying nature of user density. We show that for a single time-slot, the optimal placement is achieved when the transmit power of UAV-BSs equals their on-board circuit power. Then, for multiple time-slot duration, we prove that the optimal placement updating problem is a nonlinear dynamic programming coupled with an integer linear programming. Since the original problem is NP-hard and can not be solved with conventional recursive methods, we propose a sequential-Markovgreedy-decision method to achieve near minimal UAV-RF in polynomial time. Further, we prove that the increment of UAV-RF caused by inaccurate predicted user density is proportional to the generalization error of learned patterns. Here, in regions with large area, high-rise buildings or low user density, large sample sets are required for effective pattern formation.

Toward Big Data Processing in IoT: Path Planning and Resource Management of UAV Base Stations in Mobile-Edge Computing System

“…The position of u k is denoted as [x k , y k , h k ], where p k = [x k , y k ] represents its projection on the ground and h k is its corresponding hovering altitude. It is assumed that u k covers sensors around p k within radius r. In our previous work [15], we proved that the optimal height h * k satisfies…”

“…In Fig. 4(b), we validate the influence of balance on α in (15). Without balance on α, the training update rate is set to be equal for each candidate action.…”

“…end for Randomly choose a batch of interaction experience (o i , a i , r i , o i+1 ) from relay memory E(t p ). Determine update rate α by (15) and calculate the target value y i by…”

“…In [14], the authors jointly considered energy efficiency and user coverage to optimize UAV-BS placement. In [15], the authors considered the placement of UAV-BSs with the criterion of minimizing UAVrecall-frequency. Furthermore, UAV-BSs were also considered as a MEC server in [16].…”

A Near-Optimal UAV-Aided Radio Coverage Strategy for Dense Urban Areas

“…However, the authors of [8] and [9] only aimed for optimizing the transmit power. Given the fact that the energy consumed by the propulsion of drones is much higher than that consumed by communication, Lu et al [10] focused their attention both on the on-board circuit power and on the propulsion power requirements for the sake of minimizing the frequency of the UAV's battery charging operation. Furthermore, the authors of both [11] and [12] considered the UAV's propulsion power in the context of UAV-to-ground communications.…”