Performance Characterization of Canonical Mobility Models in Drone Cellular Networks
Performance Characterization of Canonical Mobility Models in Drone Cellular Networks
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DOI：
10.1109/twc.2020.2988633
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发表时间：
2020-07-01
影响因子：
10.4
通讯作者：
Dhillon, Harpreet S.
中科院分区：
文献类型：
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作者：
Banagar, Morteza;Dhillon, Harpreet S.
在本文中，我们描述了几种典型的移动模型在无人机蜂窝网络中的性能，其中无人机基站(DBS)服务于地面上的一组用户设备(UE)。特别是，我们考虑了以下四个移动模型：(I)直线(SL)，(Ii)随机停止(RS)，(Iii)随机行走(RW)，和(Iv)随机路点(RWP)，其中SL移动模型的灵感来自于第三代合作伙伴项目(3GPP)用于无人机放置和轨迹的模拟模型，而其他三个模型是众所周知的规范模型(或其变体)，在真实性和易操纵性之间提供了有用的平衡。在假设最近邻关联策略的情况下，我们考虑了两种UE服务模型：(I)UE独立模型(UIM)和(Ii)UE依赖模型(UDM)。虽然服务DBS遵循与UIM中的其他DBS相同的移动性模型，但假设它飞向UDM中感兴趣的UE，并在到达那里后在其位置上方悬停。本文的主要贡献是提供了一种统一的方法来刻画所有移动性和服务模型的DBS的点过程。利用这一点，我们提供了典型UE所看到的平均接收速率和会话速率的精确数学表达式。此外，利用变分法中的工具，我们具体地证明了简单的SL机动性模型提供了其他一般机动性模型(包括无人机沿曲线轨迹的模型)的性能下限，只要这些模型中每架无人机的运动是独立的和同分布的(I.I.D.)。据我们所知，这是第一项对无限无人机蜂窝网络的关键规范移动性模型进行严格分析并在它们之间建立有用联系的工作。
In this paper, we characterize the performance of several canonical mobility models in a drone cellular network in which drone base stations (DBSs) serve a set of user equipment (UE) on the ground. In particular, we consider the following four mobility models: (i) straight line (SL), (ii) random stop (RS), (iii) random walk (RW), and (iv) random waypoint (RWP), among which the SL mobility model is inspired by the simulation models used by the third generation partnership project (3GPP) for the placement and trajectory of drones, while the other three are well-known canonical models (or their variants) that offer a useful balance between realism and tractability. Assuming the nearest-neighbor association policy, we consider two service models for the UEs: (i) UE independent model (UIM), and (ii) UE dependent model (UDM). While the serving DBS follows the same mobility model as the other DBSs in the UIM, it is assumed to fly towards the UE of interest in the UDM and hover above its location after reaching there. The main contribution of this paper is a unified approach to characterize the point process of DBSs for all the mobility and service models. Using this, we provide exact mathematical expressions for the average received rate and the session rate as seen by the typical UE. Further, using tools from the calculus of variations, we concretely demonstrate that the simple SL mobility model provides a lower bound on the performance of other general mobility models (including the ones in which drones follow curved trajectories) as long as the movement of each drone in these models is independent and identically distributed (i.i.d.). To the best of our knowledge, this is the first work that provides a rigorous analysis of key canonical mobility models for an infinite drone cellular network and establishes useful connections between them.