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 transmit power control or dynamic spectrum allocation. Bearing in...

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Vydáno v:IEEE internet of things journal Ročník 8; číslo 3; s. 1623 - 1635
Hlavní autoři: Bai, Tong, Pan, Cunhua, Wang, Jingjing, Deng, Yansha, Elkashlan, Maged, Nallanathan, Arumugam, Hanzo, Lajos
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Base stations
Buffers
Delay
Delay optimal
Delays
dynamic programming
Heuristic algorithms
Internet of Things
Markov decision process (MDP)
Placement
Power control
reinforcement learning
Resource management
Spectrum allocation
Statistical analysis
Throughput
unmanned aerial vehicle (UAV)
Unmanned aerial vehicles
Vehicle dynamics
Wireless communication
ISSN:2327-4662, 2327-4662
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Popis
Shrnutí: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.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2020.3013752