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Development of deep reinforcement learning based resource allocation techniques in cloud radio access network

Nextgeneration networks are envisioned to support management to maximize the user s ’ dynamic and agile network quality of service (QoS). Cloud radio access network (CRAN) emerges as a promising candidate since the limited network resources can be virtualized and shared among distributed remote radi...

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主要作者:	Amjad, Iqbal
格式:	Final Year Project / Dissertation / Thesis
出版:	2022
主题:	T Technology (General)
在线阅读:	http://eprints.utar.edu.my/4606/1/Amjad_Iqbal.pdf http://eprints.utar.edu.my/4606/
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id	my-utar-eprints.4606
record_format	eprints
spelling	my-utar-eprints.46062022-12-29T14:19:57Z Development of deep reinforcement learning based resource allocation techniques in cloud radio access network Amjad, Iqbal T Technology (General) Nextgeneration networks are envisioned to support management to maximize the user s ’ dynamic and agile network quality of service (QoS). Cloud radio access network (CRAN) emerges as a promising candidate since the limited network resources can be virtualized and shared among distributed remote radio heads (RRHs). Conventional approaches formulate resource allocation as an optimization problem and solve it with instantaneous environment knowledge without considering the consequences of actions. A step towards long network performance optimization is theterm use of deep reinforcement learning (DRL), which can learn the best policy via interaction with the environment. This thesis proposes three DRLbased resource allocation algorithms that optimize the CRAN performance in terms of energy efficiency (EE), spec tral efficiency (SE), and total power consumption. The first proposed algorithm aims to optimize the EE by controlling the on/off status of RRH via a deep Q network (DQN) and subsequently solving a power optimization problem. To capture the spatiotemporal channel state information (CSI), the second proposed algorithm adopts machine learning techniques with anchor graph hashing to extract generalized features before feeding them into the DQN. The goal here is to optimize the longterm tradeoff between EE and SE. In the last proposed scheme, additional EE savings are facilitated by designing and integrating a convolution al neural network (CNN), which can better learn the feature of environment states. Simulation results show that all proposed DRL algorithms outperform 2025% compared to existing techniques while achieving faster convergence. All performance benchmarking was carried out based on 100 testing episodes after properly training the DRL agent with 1000 episodes. 2022 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4606/1/Amjad_Iqbal.pdf Amjad, Iqbal (2022) Development of deep reinforcement learning based resource allocation techniques in cloud radio access network. PhD thesis, UTAR. http://eprints.utar.edu.my/4606/
institution	Universiti Tunku Abdul Rahman
building	UTAR Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tunku Abdul Rahman
content_source	UTAR Institutional Repository
url_provider	http://eprints.utar.edu.my
topic	T Technology (General)
spellingShingle	T Technology (General) Amjad, Iqbal Development of deep reinforcement learning based resource allocation techniques in cloud radio access network
description	Nextgeneration networks are envisioned to support management to maximize the user s ’ dynamic and agile network quality of service (QoS). Cloud radio access network (CRAN) emerges as a promising candidate since the limited network resources can be virtualized and shared among distributed remote radio heads (RRHs). Conventional approaches formulate resource allocation as an optimization problem and solve it with instantaneous environment knowledge without considering the consequences of actions. A step towards long network performance optimization is theterm use of deep reinforcement learning (DRL), which can learn the best policy via interaction with the environment. This thesis proposes three DRLbased resource allocation algorithms that optimize the CRAN performance in terms of energy efficiency (EE), spec tral efficiency (SE), and total power consumption. The first proposed algorithm aims to optimize the EE by controlling the on/off status of RRH via a deep Q network (DQN) and subsequently solving a power optimization problem. To capture the spatiotemporal channel state information (CSI), the second proposed algorithm adopts machine learning techniques with anchor graph hashing to extract generalized features before feeding them into the DQN. The goal here is to optimize the longterm tradeoff between EE and SE. In the last proposed scheme, additional EE savings are facilitated by designing and integrating a convolution al neural network (CNN), which can better learn the feature of environment states. Simulation results show that all proposed DRL algorithms outperform 2025% compared to existing techniques while achieving faster convergence. All performance benchmarking was carried out based on 100 testing episodes after properly training the DRL agent with 1000 episodes.
format	Final Year Project / Dissertation / Thesis
author	Amjad, Iqbal
author_facet	Amjad, Iqbal
author_sort	Amjad, Iqbal
title	Development of deep reinforcement learning based resource allocation techniques in cloud radio access network
title_short	Development of deep reinforcement learning based resource allocation techniques in cloud radio access network
title_full	Development of deep reinforcement learning based resource allocation techniques in cloud radio access network
title_fullStr	Development of deep reinforcement learning based resource allocation techniques in cloud radio access network
title_full_unstemmed	Development of deep reinforcement learning based resource allocation techniques in cloud radio access network
title_sort	development of deep reinforcement learning based resource allocation techniques in cloud radio access network
publishDate	2022
url	http://eprints.utar.edu.my/4606/1/Amjad_Iqbal.pdf http://eprints.utar.edu.my/4606/
_version_	1753793021423386624
score	13.251813

Development of deep reinforcement learning based resource allocation techniques in cloud radio access network

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