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Beam Management and Self-Healing for mmWave UAV Mesh Networks
Author(s) -
Pei Zhou,
Xuming Fang,
Yuguang Fang,
Rong He,
Yan Long,
Gaoyong Huang
Publication year - 2018
Publication title -
ieee transactions on vehicular technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.365
H-Index - 178
eISSN - 1939-9359
pISSN - 0018-9545
DOI - 10.1109/tvt.2018.2890152
Subject(s) - robustness (evolution) , base station , computer science , computer network , relay , overhead (engineering) , mobility management , distributed computing , real time computing , biochemistry , chemistry , power (physics) , physics , quantum mechanics , gene , operating system
The point-to-point and point-to-multipoint communication features in the IEEE 802.11ay make it possible for unmanned aerial vehicles (UAVs) to form a UAV mesh network (flyMesh). In an 802.11ay based millimeter-wave (mmWave) flyMesh, many challenges are brought by the collaborative interactions of UAVs and the beam misalignment of the directional communication links. This paper studies how to guarantee the robustness of the mmWave flyMesh with respect to beam management and network self-healing. First, to cope with the problem of beam misalignment between UAVs or between the UAV group leader and relay base station/satellite caused by the movement of UAV(s), we propose a fast beam tracking mechanism. Second, in order to solve the problem of link failures due to departures of some UAVs from the group or failures, the self-healing mechanism is proposed to find alternative links to restore the network capability. Finally, to deal with the dynamic group leader changes of UAVs due to mobility, we propose an efficient UAV group leader re-selection mechanism to reduce the overhead of UAV group management. Through performance analysis and simulation, we demonstrate that our proposed mechanisms can effectively address the aforementioned problems and challenges.

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