東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Analyzing Usage Conflict Situations in Localized Spectrum Sharing Scenarios: An Agent-based Modeling and Machine Learning Approach.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analyzing Usage Conflict Situations in Localized Spectrum Sharing Scenarios: An Agent-based Modeling and Machine Learning Approach./
Author:	Bustamante, Pedro J. .
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	349 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
Subject:	Maps. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29057018
ISBN:	9798426863910

Analyzing Usage Conflict Situations in Localized Spectrum Sharing Scenarios: An Agent-based Modeling and Machine Learning Approach.
Bustamante, Pedro J. .

Analyzing Usage Conflict Situations in Localized Spectrum Sharing Scenarios: An Agent-based Modeling and Machine Learning Approach. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 349 p.

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--University of Pittsburgh, 2021.

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

As spectrum sharing matures, different approaches have been proposed for a more efficient allocation, assignment, and usage of spectrum resources. These approaches include cognitive radios, multi-level user definitions, radio environment maps, among others. However, spectrum usage conflicts (e.g., "harmful" interference) remain a common challenge in spectrum sharing schemes. In particular, in conflict situations where it is necessary to take actions to ensure the sound operations of sharing agreements. A typical example of a usage conflict is where incumbents' tolerable levels of interference (i.e., interference thresholds) are surpassed. In this work, we present a new method to examine and study spectrum usage conflicts. A fundamental goal of this project is to capture local resource usage patterns to provide more realistic estimates of interference. For this purpose, we have defined two spectrum and network-specific characteristics that directly impact the local interference assessment: resource access strategy and governance framework. Thus, we are able to test the viability in spectrum sharing situations of distributed or decentralized governance systems, including polycentric and self-governance. In addition, we are able to design, model, and test a multi-tier spectrum sharing scheme that provides stakeholders with more flexible resource access opportunities.To perform this dynamic and localized study of spectrum usage and conflicts, we rely on Agent-Based Modeling (ABM) as our main analysis instrument. A crucial component for capturing local resource usage patterns is to provide agents with local information about their spectrum situation. Thus, the environment of the models presented in this dissertation are given by the REM's Interference Cartography (IC) map. Additionally, the agents' definitions and actions are the results of the interaction of the technical aspects of resource access and management, stakeholder interactions, and the underlying usage patterns as defined in the Common Pool Resource (CPR) literature. Finally, to capture local resource usage patterns and, consequently, provide more realistic estimates of conflict situations, we enhance the classical rule-based ABM approach by using Machine Learning (ML) techniques. Via ML algorithms, we refine the internal models of agents in an ABM. Thus, the agents' internal models allow them to choose more suitable responses to changes in the environment.

ISBN: 9798426863910Subjects--Topical Terms:

544078
Maps.

Analyzing Usage Conflict Situations in Localized Spectrum Sharing Scenarios: An Agent-based Modeling and Machine Learning Approach.
LDR:03537nmm a2200313 4500 001 2352191
005 20221118093838.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798426863910
035 $a (MiAaPQ)AAI29057018
035 $a (MiAaPQ)Pittsburgh42128
035 $a AAI29057018
040 $a MiAaPQ $c MiAaPQ
100 1 $a Bustamante, Pedro J. . $3 3691813
245 1 0 $a Analyzing Usage Conflict Situations in Localized Spectrum Sharing Scenarios: An Agent-based Modeling and Machine Learning Approach.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 349 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
500 $a Advisor: Weiss, Martin B. H. .
502 $a Thesis (Ph.D.)--University of Pittsburgh, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a As spectrum sharing matures, different approaches have been proposed for a more efficient allocation, assignment, and usage of spectrum resources. These approaches include cognitive radios, multi-level user definitions, radio environment maps, among others. However, spectrum usage conflicts (e.g., "harmful" interference) remain a common challenge in spectrum sharing schemes. In particular, in conflict situations where it is necessary to take actions to ensure the sound operations of sharing agreements. A typical example of a usage conflict is where incumbents' tolerable levels of interference (i.e., interference thresholds) are surpassed. In this work, we present a new method to examine and study spectrum usage conflicts. A fundamental goal of this project is to capture local resource usage patterns to provide more realistic estimates of interference. For this purpose, we have defined two spectrum and network-specific characteristics that directly impact the local interference assessment: resource access strategy and governance framework. Thus, we are able to test the viability in spectrum sharing situations of distributed or decentralized governance systems, including polycentric and self-governance. In addition, we are able to design, model, and test a multi-tier spectrum sharing scheme that provides stakeholders with more flexible resource access opportunities.To perform this dynamic and localized study of spectrum usage and conflicts, we rely on Agent-Based Modeling (ABM) as our main analysis instrument. A crucial component for capturing local resource usage patterns is to provide agents with local information about their spectrum situation. Thus, the environment of the models presented in this dissertation are given by the REM's Interference Cartography (IC) map. Additionally, the agents' definitions and actions are the results of the interaction of the technical aspects of resource access and management, stakeholder interactions, and the underlying usage patterns as defined in the Common Pool Resource (CPR) literature. Finally, to capture local resource usage patterns and, consequently, provide more realistic estimates of conflict situations, we enhance the classical rule-based ABM approach by using Machine Learning (ML) techniques. Via ML algorithms, we refine the internal models of agents in an ABM. Thus, the agents' internal models allow them to choose more suitable responses to changes in the environment.
590 $a School code: 0178.
650 4 $a Maps. $3 544078
650 4 $a Agreements. $3 3559354
650 4 $a Cartography. $3 543508
650 4 $a Algorithms. $3 536374
650 4 $a Sensitivity analysis. $3 3560752
650 4 $a Artificial intelligence. $3 516317
650 4 $a Computer science. $3 523869
690 $a 0800
690 $a 0984
710 2 $a University of Pittsburgh. $3 958527
773 0 $t Dissertations Abstracts International $g 83-11B.
790 $a 0178
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29057018