Dynamic Aerial Base Station Placement for Minimum-Delay Communications

Tong Bai, Cunhua Pan, Jingjing Wang, Yansha Deng, Maged Elkashlan, Arumugam Nallanathan, Lajos Hanzo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


Queuing delay is of essential importance in the Internet-of-Things scenarios where the buffer sizes of devices are limited. The existing cross-layer research contributions aiming at minimizing the queuing delay usually rely on either transmit power control or dynamic spectrum allocation. Bearing in mind that the transmission throughput is dependent on the distance between the transmitter and the receiver, in this context we exploit the agility of the unmanned-aerial-vehicle (UAV)-mounted base stations (BSs) for proactively adjusting the aerial BS (ABS)'s placement in accordance with wireless teletraffic dynamics. Specifically, we formulate a minimum-delay ABS placement problem for UAV-enabled networks, subject to realistic constraints on the ABS's battery life and velocity. Its solutions are technically realized under three different assumptions in regard to the wireless teletraffic dynamics. The backward induction technique is invoked for both the scenario where the full knowledge of the wireless teletraffic dynamics is available, and for the case where only their statistical knowledge is available. In contrast, a reinforcement learning aided approach is invoked for the case when neither the exact number of arriving packets nor that of their statistical knowledge is available. The numerical results demonstrate that our proposed algorithms are capable of improving the system's performance compared to the benchmark schemes in terms of both the average delay and of the buffer overflow probability.

Original languageEnglish
Article number9154460
Pages (from-to)1623-1635
Number of pages13
JournalIEEE Internet of Things Journal
Issue number3
Publication statusPublished - 1 Feb 2021


  • Delay optimal
  • dynamic programming
  • Markov decision process (MDP)
  • reinforcement learning
  • unmanned aerial vehicle (UAV)


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