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Dynamic aerial base station placement for minimum-delay communications

Dynamic aerial base station placement for minimum-delay communications
Dynamic aerial base station placement for minimum-delay communications
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.
2327-4662
Bai, Tong
58cbdac7-1cea-4346-8fe2-70b4c9f2cc16
Pan, Cunhua
f7d52330-7fd8-42eb-8a5a-e094829a9fea
Wang, Jingjing
45786e24-b847-4830-a2f3-18ba61a9fb29
Deng, Yansha
66af8713-790e-4b21-a99a-1114ac762059
Elkashlan, Maged
27c756ff-bfd3-4844-8769-ace5ad28c840
Nallanathan, Arumugam
d255cda5-a015-4bb9-9f17-88614a544396
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Bai, Tong
58cbdac7-1cea-4346-8fe2-70b4c9f2cc16
Pan, Cunhua
f7d52330-7fd8-42eb-8a5a-e094829a9fea
Wang, Jingjing
45786e24-b847-4830-a2f3-18ba61a9fb29
Deng, Yansha
66af8713-790e-4b21-a99a-1114ac762059
Elkashlan, Maged
27c756ff-bfd3-4844-8769-ace5ad28c840
Nallanathan, Arumugam
d255cda5-a015-4bb9-9f17-88614a544396
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Bai, Tong, Pan, Cunhua, Wang, Jingjing, Deng, Yansha, Elkashlan, Maged, Nallanathan, Arumugam and Hanzo, Lajos (2020) Dynamic aerial base station placement for minimum-delay communications. IEEE Internet of Things Journal. (doi:10.1109/JIOT.2020.3013752).

Record type: Article

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.

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Accepted/In Press date: 30 July 2020
e-pub ahead of print date: 3 August 2020

Identifiers

Local EPrints ID: 443131
URI: http://eprints.soton.ac.uk/id/eprint/443131
ISSN: 2327-4662
PURE UUID: 6d1ab918-c378-4fe7-9a5a-e7e2256f081d
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 11 Aug 2020 16:34
Last modified: 13 Aug 2022 01:32

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Contributors

Author: Tong Bai
Author: Cunhua Pan
Author: Jingjing Wang
Author: Yansha Deng
Author: Maged Elkashlan
Author: Arumugam Nallanathan
Author: Lajos Hanzo ORCID iD

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