Power allocation and energy cooperation for UAV-enabled MmWave networks: A Multi-Agent Deep Reinforcement Learning approach

Unmanned Aerial Vehicle (UAV)-assisted cellular networks over the millimeter-wave (mmWave) frequency band can meet the requirements of a high data rate and flexible coverage in next-generation communication networks. However, higher propagation loss and the use of a large number of antennas in mmWav...

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Autor: Domingo Aladrén, Mari Carmen|||0000-0002-6901-3817
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/363156
Acceso en línea:https://hdl.handle.net/2117/363156
https://dx.doi.org/10.3390/s22010270
Access Level:acceso abierto
Palabra clave:Drone aircraft
Unmanned Aerial Vehicles (UAVs)
energy harvesting
energy cooperation
power allocation
Multi-Agent Deep Reinforcement Learning (MADDPG)
Avions no tripulats
Àrees temàtiques de la UPC::Aeronàutica i espai::Aeronaus
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oai_identifier_str oai:upcommons.upc.edu:2117/363156
network_acronym_str ES
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repository_id_str
spelling Power allocation and energy cooperation for UAV-enabled MmWave networks: A Multi-Agent Deep Reinforcement Learning approachDomingo Aladrén, Mari Carmen|||0000-0002-6901-3817Drone aircraftUnmanned Aerial Vehicles (UAVs)energy harvestingenergy cooperationpower allocationMulti-Agent Deep Reinforcement Learning (MADDPG)Avions no tripulatsÀrees temàtiques de la UPC::Aeronàutica i espai::AeronausUnmanned Aerial Vehicle (UAV)-assisted cellular networks over the millimeter-wave (mmWave) frequency band can meet the requirements of a high data rate and flexible coverage in next-generation communication networks. However, higher propagation loss and the use of a large number of antennas in mmWave networks give rise to high energy consumption and UAVs are constrained by their low-capacity onboard battery. Energy harvesting (EH) is a viable solution to reduce the energy cost of UAV-enabled mmWave networks. However, the random nature of renewable energy makes it challenging to maintain robust connectivity in UAV-assisted terrestrial cellular networks. Energy cooperation allows UAVs to send their excessive energy to other UAVs with reduced energy. In this paper, we propose a power allocation algorithm based on energy harvesting and energy cooperation to maximize the throughput of a UAV-assisted mmWave cellular network. Since there is channel-state uncertainty and the amount of harvested energy can be treated as a stochastic process, we propose an optimal multi-agent deep reinforcement learning algorithm (DRL) named Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to solve the renewable energy resource allocation problem for throughput maximization. The simulation results show that the proposed algorithm outperforms the Random Power (RP), Maximal Power (MP) and value-based Deep Q-Learning (DQL) algorithms in terms of network throughput.This work was supported by the Agencia Estatal de Investigación of Ministerio de Ciencia e Innovación of Spain under project PID2019-108713RB-C51 MCIN/AEI /10.13039/50110001103320212021-12-3020222022-02-28journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/363156https://dx.doi.org/10.3390/s22010270reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 3.0 Spainhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3631562026-05-27T15:37:01Z
dc.title.none.fl_str_mv Power allocation and energy cooperation for UAV-enabled MmWave networks: A Multi-Agent Deep Reinforcement Learning approach
title Power allocation and energy cooperation for UAV-enabled MmWave networks: A Multi-Agent Deep Reinforcement Learning approach
spellingShingle Power allocation and energy cooperation for UAV-enabled MmWave networks: A Multi-Agent Deep Reinforcement Learning approach
Domingo Aladrén, Mari Carmen|||0000-0002-6901-3817
Drone aircraft
Unmanned Aerial Vehicles (UAVs)
energy harvesting
energy cooperation
power allocation
Multi-Agent Deep Reinforcement Learning (MADDPG)
Avions no tripulats
Àrees temàtiques de la UPC::Aeronàutica i espai::Aeronaus
title_short Power allocation and energy cooperation for UAV-enabled MmWave networks: A Multi-Agent Deep Reinforcement Learning approach
title_full Power allocation and energy cooperation for UAV-enabled MmWave networks: A Multi-Agent Deep Reinforcement Learning approach
title_fullStr Power allocation and energy cooperation for UAV-enabled MmWave networks: A Multi-Agent Deep Reinforcement Learning approach
title_full_unstemmed Power allocation and energy cooperation for UAV-enabled MmWave networks: A Multi-Agent Deep Reinforcement Learning approach
title_sort Power allocation and energy cooperation for UAV-enabled MmWave networks: A Multi-Agent Deep Reinforcement Learning approach
dc.creator.none.fl_str_mv Domingo Aladrén, Mari Carmen|||0000-0002-6901-3817
author Domingo Aladrén, Mari Carmen|||0000-0002-6901-3817
author_facet Domingo Aladrén, Mari Carmen|||0000-0002-6901-3817
author_role author
dc.subject.none.fl_str_mv Drone aircraft
Unmanned Aerial Vehicles (UAVs)
energy harvesting
energy cooperation
power allocation
Multi-Agent Deep Reinforcement Learning (MADDPG)
Avions no tripulats
Àrees temàtiques de la UPC::Aeronàutica i espai::Aeronaus
topic Drone aircraft
Unmanned Aerial Vehicles (UAVs)
energy harvesting
energy cooperation
power allocation
Multi-Agent Deep Reinforcement Learning (MADDPG)
Avions no tripulats
Àrees temàtiques de la UPC::Aeronàutica i espai::Aeronaus
description Unmanned Aerial Vehicle (UAV)-assisted cellular networks over the millimeter-wave (mmWave) frequency band can meet the requirements of a high data rate and flexible coverage in next-generation communication networks. However, higher propagation loss and the use of a large number of antennas in mmWave networks give rise to high energy consumption and UAVs are constrained by their low-capacity onboard battery. Energy harvesting (EH) is a viable solution to reduce the energy cost of UAV-enabled mmWave networks. However, the random nature of renewable energy makes it challenging to maintain robust connectivity in UAV-assisted terrestrial cellular networks. Energy cooperation allows UAVs to send their excessive energy to other UAVs with reduced energy. In this paper, we propose a power allocation algorithm based on energy harvesting and energy cooperation to maximize the throughput of a UAV-assisted mmWave cellular network. Since there is channel-state uncertainty and the amount of harvested energy can be treated as a stochastic process, we propose an optimal multi-agent deep reinforcement learning algorithm (DRL) named Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to solve the renewable energy resource allocation problem for throughput maximization. The simulation results show that the proposed algorithm outperforms the Random Power (RP), Maximal Power (MP) and value-based Deep Q-Learning (DQL) algorithms in terms of network throughput.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-12-30
2022
2022-02-28
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/363156
https://dx.doi.org/10.3390/s22010270
url https://hdl.handle.net/2117/363156
https://dx.doi.org/10.3390/s22010270
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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