dc.contributor.author | Bai, T | |
dc.contributor.author | Pan, C | |
dc.contributor.author | Wang, J | |
dc.contributor.author | Deng, Y | |
dc.contributor.author | Elkashlan, M | |
dc.contributor.author | Nallanathan, A | |
dc.contributor.author | Hanzo, L | |
dc.date.accessioned | 2021-05-13T14:38:26Z | |
dc.date.available | 2021-05-13T14:38:26Z | |
dc.date.issued | 2020-08-01 | |
dc.identifier.issn | 2327-4662 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/71793 | |
dc.description.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 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. | en_US |
dc.format.extent | 1623 - 1635 | |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | IEEE INTERNET OF THINGS JOURNAL | |
dc.subject | Wireless communication | en_US |
dc.subject | Delays | en_US |
dc.subject | Throughput | en_US |
dc.subject | Vehicle dynamics | en_US |
dc.subject | Base stations | en_US |
dc.subject | Heuristic algorithms | en_US |
dc.subject | Resource management | en_US |
dc.subject | Delay optimal | en_US |
dc.subject | dynamic programming | en_US |
dc.subject | Markov decision process (MDP) | en_US |
dc.subject | reinforcement learning | en_US |
dc.subject | unmanned aerial vehicle (UAV) | en_US |
dc.title | Dynamic Aerial Base Station Placement for Minimum-Delay Communications | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
dc.identifier.doi | 10.1109/JIOT.2020.3013752 | |
pubs.author-url | http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000612146000029&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6a | en_US |
pubs.issue | 3 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 8 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |