Dynamic aerial base station placement for minimum-delay communications

Abstract

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 trans- mit 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 for proactively adjusting the aerial base station (ABS)’s placement in accordance with wireless tele-traffic 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 tele-traffic dynamics. The backward induction technique is invoked for both the scenario where the full knowledge of the wireless tele-traffic dynamics is available, and for the case where only their statistical knowledge is available. By 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.

More information

Identifiers

ORCID for Lajos Hanzo: orcid.org/0000-0002-2636-5214

Catalogue record

Export record

Altmetrics