In modern networks, the use of drones as mobile base stations (MBSs) has been discussed for coverage flexibility. However, the realization of drone-based networks raises several issues. One of critical issues is drones are extremely power-hungry. To overcome this, we need to characterize a new type of drones, so-called charging drones, which can deliver energy to MBS drones. Motivated by the fact that the charging drones also need to be charged, we deploy ground-mounted charging towers for delivering energy to the charging drones. We introduce a new energy-efficiency maximization problem, which is partitioned into two independently separable tasks. More specifically, as our first optimization task, two-stage charging matching is proposed due to the inherent nature of our network model, where the first matching aims to schedule between charging towers and charging drones while the second matching solves the scheduling between charging drones and MBS drones. We analyze how to convert the formulation containing non-convex terms to another one only with convex terms. As our second optimization task, each MBS drone conducts energy-aware time-average transmit power allocation minimization subject to stability via Lyapunov optimization. Our solutions enable the MBS drones to extend their lifetimes; in turn, network coverage-time can be extended.
|Number of pages||11|
|Publication status||Published - 2021|
Bibliographical noteFunding Information:
Corresponding authors: Won-Yong Shin (firstname.lastname@example.org), Minseok Choi (email@example.com), and Joongheon Kim (firstname.lastname@example.org) This work was supported in part by the National Research Foundation of Korea (NRF) under Grant 2021R1A2C3004345 and Grant 2020R1G1A1101164, in part by the Institute for Information and Communications Technology Promotion (IITP) by MSIT under Grant 2018-0-00170, in part by the Virtual Presence in Moving Objects through 5G under Grant 2020R1G1A1101164, and in part by the National University Development Project by the Ministry of Education (South Korea) and NRF (2021).
© 2013 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)