Department of Electronics and Communication Engineering, Istanbul Technical University (ITU), 34467 Istanbul, Turkey.
Wireless Communication Centre, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM, Johor Bahru 81310, Johor, Malaysia.
Sensors (Basel). 2022 Aug 25;22(17):6424. doi: 10.3390/s22176424.
Drones have attracted extensive attention for their environmental, civil, and military applications. Because of their low cost and flexibility in deployment, drones with communication capabilities are expected to play key important roles in Fifth Generation (5G), Sixth Generation (6G) mobile networks, and beyond. 6G and 5G are intended to be a full-coverage network capable of providing ubiquitous connections for space, air, ground, and underwater applications. Drones can provide airborne communication in a variety of cases, including as Aerial Base Stations (ABSs) for ground users, relays to link isolated nodes, and mobile users in wireless networks. However, variables such as the drone's free-space propagation behavior at high altitudes and its exposure to antenna sidelobes can contribute to radio environment alterations. These differences may render existing mobility models and techniques as inefficient for connected drone applications. Therefore, drone connections may experience significant issues due to limited power, packet loss, high network congestion, and/or high movement speeds. More issues, such as frequent handovers, may emerge due to erroneous transmissions from limited coverage areas in drone networks. Therefore, the deployments of drones in future mobile networks, including 5G and 6G networks, will face a critical technical issue related to mobility and handover processes due to the main differences in drones' characterizations. Therefore, drone networks require more efficient mobility and handover techniques to continuously maintain stable and reliable connection. More advanced mobility techniques and system reconfiguration are essential, in addition to an alternative framework to handle data transmission. This paper reviews numerous studies on handover management for connected drones in mobile communication networks. The work contributes to providing a more focused review of drone networks, mobility management for drones, and related works in the literature. The main challenges facing the implementation of connected drones are highlighted, especially those related to mobility management, in more detail. The analysis and discussion of this study indicates that, by adopting intelligent handover schemes that utilizing machine learning, deep learning, and automatic robust processes, the handover problems and related issues can be reduced significantly as compared to traditional techniques.
无人机因其在环境、民用和军事方面的应用而受到广泛关注。由于其成本低、部署灵活,具有通信能力的无人机有望在第五代(5G)、第六代(6G)移动网络及以后发挥关键作用。6G 和 5G 旨在成为一个全覆盖网络,能够为空间、空中、地面和水下应用提供无处不在的连接。无人机可以在多种情况下提供空中通信,例如为地面用户提供空中基站(ABS)、作为中继器连接隔离节点以及作为无线网络中的移动用户。然而,无人机在高空的自由空间传播行为及其对天线旁瓣的暴露等变量可能会导致无线电环境发生变化。这些差异可能会使现有的移动性模型和技术在连接的无人机应用中效率低下。因此,由于功率有限、数据包丢失、网络拥塞高和/或移动速度高,无人机连接可能会遇到重大问题。由于无人机网络中有限覆盖区域的错误传输,可能会出现更多问题,例如频繁切换。因此,由于无人机特性的主要差异,未来移动网络(包括 5G 和 6G 网络)中部署的无人机将面临与移动性和切换过程相关的关键技术问题。因此,无人机网络需要更有效的移动性和切换技术来持续保持稳定可靠的连接。除了需要替代的数据传输框架外,还需要更先进的移动性技术和系统配置。本文综述了移动通信网络中连接的无人机切换管理的大量研究。这项工作有助于提供对无人机网络、无人机移动性管理以及文献中相关工作的更集中的回顾。更详细地突出了实现联网无人机所面临的主要挑战,特别是与移动性管理相关的挑战。对这项研究的分析和讨论表明,与传统技术相比,通过采用利用机器学习、深度学习和自动鲁棒过程的智能切换方案,可以显著减少切换问题和相关问题。