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Towards Green Communications: Energy Efficient Solutions for the Next Generation Cellular Mobile Communication Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards Green Communications: Energy Efficient Solutions for the Next Generation Cellular Mobile Communication Systems./
Author:	Wang, Luhao.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	124 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
Contained By:	Dissertations Abstracts International81-10B.
Subject:	Information technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27787021
ISBN:	9781392442715

Towards Green Communications: Energy Efficient Solutions for the Next Generation Cellular Mobile Communication Systems.
Wang, Luhao.

Towards Green Communications: Energy Efficient Solutions for the Next Generation Cellular Mobile Communication Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 124 p.

Source: Dissertations Abstracts International, Volume: 81-10, Section: B.

Thesis (Ph.D.)--University of Southern California, 2019.

This item must not be sold to any third party vendors.

The proliferation of multimedia infotainment applications and smart consumer electronics (e.g., cellphones, tablets, wearable devices, laptops) has remarkably impacted how we entertain, interact and communicate. Video streaming, HDTV and social networks fuel the exponential growth of global mobile data. Consequently, current network systems already reach their capacity limits in highly populated area during peak hours. Congestion problems arise especially in the backhaul links. Academia and industry in the field of communications have reached a consensus that incremental improvements on the existing infrastructure fail to meet the explosive data demands of the foreseeable future. The goals of next generation mobile network (5G) are broad and are presumed to include much greater throughput, much lower latency, and lower power consumption. Promising solutions such as heterogeneous network (HetNet), cell sleeping techniques, cache-aided base stations (BSs) and renewable energy power supply have been proposed and deployed to address those challenges. Although the deployment of dense small cell base stations (sBSs) in HetNet can efficiently offload traffic from the existed macro cell base stations (MBSs), the further benefits have not be harnessed well enough and the potential overhead should be carefully alleviated.This thesis aims to address these challenges by embracing novel mathematical frameworks in control theory, statistical machine learning and probabilistic graph model and advocate for intelligent, energy-efficient and low complexity methodologies for the management of entities (BSs, user equipments, power beacon) for next generation network systems. This thesis proposes a variational inference (VI) based Bayesian neural network (BNN) to learn from user's contexts to drive the decision of dynamically switching an sBS between the active mode and the sleep mode to minimize the total energy consumption by solving a multi-armed bandit problem. This distributed approach is independent of cache updating policy and can adaptively learn from the requests from users. A posterior distributions for every weights and biases are updated by VI which is efficient especially for neural networks with large scales. No special parameter tuning is needed for the scenarios that user's preferences change rapidly.This thesis derives an efficient online near-optimal control policy with proved optimality gap for cooperative BSs powered solely by renewable energy in HetNet by Lyapunov optimization theory and convex optimization. In our studied framework, all BSs are powered by energy harvesting modules and rechargeable battery packs are deployed to deal with the intermittent energy supply, such as solar energy, so that harvested energy can be used at a later time. A virtual queue and an energy storage queue are maintained at each BS node for the purpose of balancing QoS and preventing the quick depletion of battery. This is achieved by solving their independent energy minimization sub-problems before making an active/sleep decision. The developed efficient and scalable framework can maximize the average throughput with a guaranteed optimality gap by avoiding frequent power outage.This thesis proposes a distributed framework for a cellular network consisting of one MBS and a set of sBSs and user equipments (UEs). UEs request to download files from BSs through the down-link. We jointly consider the problem of user association and dynamic-switching of BSs. The energy minimization problem with power and channel constraints is formulated as an integer non-linear programming problem. Then, a belief propagation based distributed approach is proposed to solve this problem. The effectiveness of the proposed framework in practice is demonstrated by experimental results based on realistic user request traces by outperforming other baseline algorithms.In further, a throughput maximization problem in wireless powered IoT network systems is investigated. This work proposes an opportunistic joint transmission and charging management framework. This work considers a wireless powered IoT network system consisting of a power beacon (PB), a set of IoT UEs and a BS. The PB transmits RF energy beams to IoT UEs. Each UE is equipped with a battery of limited capacity and is solely powered by the harvested energy provided by the PB. Then, a online opportunistic control solution is proposed and its optimality gap compared to the optimal solution has also been proved. The average throughput is improved against the greedy algorithm baseline.In summary, this PhD work explores energy efficient solutions for the next generation network systems. Several power management and resource allocation policies with different capabilities and under different system scenarios have been carefully investigated. With the theoretical and computational contribution combined, this PhD work aims to serve as the basis for the intelligent core of future autonomous network communication with self-organizing, self-learning and self-optimization capabilities towards the goals of high throughput, low latency and energy-efficient future network systems.

ISBN: 9781392442715Subjects--Topical Terms:

532993
Information technology.
Subjects--Index Terms:

Next generation network systems

Towards Green Communications: Energy Efficient Solutions for the Next Generation Cellular Mobile Communication Systems.
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300 $a 124 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
500 $a Advisor: Pedram, Massoud.
502 $a Thesis (Ph.D.)--University of Southern California, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a The proliferation of multimedia infotainment applications and smart consumer electronics (e.g., cellphones, tablets, wearable devices, laptops) has remarkably impacted how we entertain, interact and communicate. Video streaming, HDTV and social networks fuel the exponential growth of global mobile data. Consequently, current network systems already reach their capacity limits in highly populated area during peak hours. Congestion problems arise especially in the backhaul links. Academia and industry in the field of communications have reached a consensus that incremental improvements on the existing infrastructure fail to meet the explosive data demands of the foreseeable future. The goals of next generation mobile network (5G) are broad and are presumed to include much greater throughput, much lower latency, and lower power consumption. Promising solutions such as heterogeneous network (HetNet), cell sleeping techniques, cache-aided base stations (BSs) and renewable energy power supply have been proposed and deployed to address those challenges. Although the deployment of dense small cell base stations (sBSs) in HetNet can efficiently offload traffic from the existed macro cell base stations (MBSs), the further benefits have not be harnessed well enough and the potential overhead should be carefully alleviated.This thesis aims to address these challenges by embracing novel mathematical frameworks in control theory, statistical machine learning and probabilistic graph model and advocate for intelligent, energy-efficient and low complexity methodologies for the management of entities (BSs, user equipments, power beacon) for next generation network systems. This thesis proposes a variational inference (VI) based Bayesian neural network (BNN) to learn from user's contexts to drive the decision of dynamically switching an sBS between the active mode and the sleep mode to minimize the total energy consumption by solving a multi-armed bandit problem. This distributed approach is independent of cache updating policy and can adaptively learn from the requests from users. A posterior distributions for every weights and biases are updated by VI which is efficient especially for neural networks with large scales. No special parameter tuning is needed for the scenarios that user's preferences change rapidly.This thesis derives an efficient online near-optimal control policy with proved optimality gap for cooperative BSs powered solely by renewable energy in HetNet by Lyapunov optimization theory and convex optimization. In our studied framework, all BSs are powered by energy harvesting modules and rechargeable battery packs are deployed to deal with the intermittent energy supply, such as solar energy, so that harvested energy can be used at a later time. A virtual queue and an energy storage queue are maintained at each BS node for the purpose of balancing QoS and preventing the quick depletion of battery. This is achieved by solving their independent energy minimization sub-problems before making an active/sleep decision. The developed efficient and scalable framework can maximize the average throughput with a guaranteed optimality gap by avoiding frequent power outage.This thesis proposes a distributed framework for a cellular network consisting of one MBS and a set of sBSs and user equipments (UEs). UEs request to download files from BSs through the down-link. We jointly consider the problem of user association and dynamic-switching of BSs. The energy minimization problem with power and channel constraints is formulated as an integer non-linear programming problem. Then, a belief propagation based distributed approach is proposed to solve this problem. The effectiveness of the proposed framework in practice is demonstrated by experimental results based on realistic user request traces by outperforming other baseline algorithms.In further, a throughput maximization problem in wireless powered IoT network systems is investigated. This work proposes an opportunistic joint transmission and charging management framework. This work considers a wireless powered IoT network system consisting of a power beacon (PB), a set of IoT UEs and a BS. The PB transmits RF energy beams to IoT UEs. Each UE is equipped with a battery of limited capacity and is solely powered by the harvested energy provided by the PB. Then, a online opportunistic control solution is proposed and its optimality gap compared to the optimal solution has also been proved. The average throughput is improved against the greedy algorithm baseline.In summary, this PhD work explores energy efficient solutions for the next generation network systems. Several power management and resource allocation policies with different capabilities and under different system scenarios have been carefully investigated. With the theoretical and computational contribution combined, this PhD work aims to serve as the basis for the intelligent core of future autonomous network communication with self-organizing, self-learning and self-optimization capabilities towards the goals of high throughput, low latency and energy-efficient future network systems.
590 $a School code: 0208.
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653 $a Next generation network systems
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710 2 $a University of Southern California. $3 700129
773 0 $t Dissertations Abstracts International $g 81-10B.
790 $a 0208
791 $a Ph.D.
792 $a 2019
793 $a English
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