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Filtering by: Creator Zhang, Zhi Remove constraint Creator: Zhang, Zhi Advisor Hamdaoui, Bechir Remove constraint Advisor: Hamdaoui, Bechir Resource Type Masters Thesis Remove constraint Resource Type: Masters Thesis

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Leveraging Multiple Transmissions and Receptions for Channel-Agnostic Wireless Device Identification

Creator:

Basha, Nora

Abstract:

Enabling accurate and automated identification of wireless devices is critical for ensuring secure and authenticated data communication in large-scale networks such as IoT networks. In the aim of devising practical identification techniques that are immune to spoofing, hardware-driven RF fingerprinting using deep neural networks, which leverages the inevitable presence of...

Resource Type:

Masters Thesis
Leveraging Compressive Sampling and Machine Learning for Adaptive and Cooperative Wideband Spectrum Sensing

Creator:

Zaid, Adem

Abstract:

This thesis proposes a novel technique that exploits spectrum occupancy behaviors inherent to wideband spectrum access to enable efficient cooperative spectrum sensing. The proposed technique reduces the number of required sensing measurements while accurately recovering spectrum occupancy information. It does so by leveraging compressive sampling theory to exploit the block-like...

Resource Type:

Masters Thesis

Full Text:

. IEEE, 2017. [27] Khaled Ben Letaief and Wei Zhang. Cooperative communications for cognitive radio
Towards Long Short Term Memory Based Proactive In-Network Caching for Cloud RANs

Creator:

Tirupathi, Chittibabu

Abstract:

The rapid population growth in large urban cities has led to an unprecedented increase in both the number and the diversity of wireless devices and applications with varying quality of service requirements in terms of latency and data rates. LinkNYC is an example of an urban communication network infrastructure, which...

Resource Type:

Masters Thesis

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Zhang, Ashley Greenacre, Kai Ovesen, Kevin Bergman, and Bechir Hamdaoui. When clones flock near the fog
Towards Deep Learning Model Portability for Domain-Agnostic Device Fingerprinting

Creator:

Gaskin, Jared

Abstract:

In recent years, RF (Radio Frequency) device fingerprinting using deep learning has emerged as a method of identifying devices solely by their RF transmissions. Conventional approaches to this type of device fingerprinting are not portable to different domains. That is, if a model for this purpose is trained on data...

Resource Type:

Masters Thesis

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Academic Affiliation

Electrical Engineering and Computer Science4

Advisor

Hamdaoui, Bechir[remove]4
Chen, Lizhong1

Commencement Year

Commencement Year range begin – Commencement Year range end

Current results range from 2017 to 2023

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Committee Member

Chen, Lizhong2
Lee, Ben2
Bobba, Rakesh1
Cheng, Larry1
Cotilla-Sanchez, Eduardo1
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Creator

Basha, Nora1
Gaskin, Jared1
Tirupathi, Chittibabu1
Zaid, Adem1

Date

Date range begin – Date range end

Current results range from 2017 to 2022

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Decade

2020-20292
2010-20192

Degree Field

Computer Science2
Electrical and Computer Engineering2

Degree Level

Master's4

Degree Name

Master of Science (M.S.)4

Language

English [eng]4

License

All rights reserved4

Non-Academic Affiliation

Oregon State University. Graduate School1

Peer Reviewed

No4

Resource Type

Masters Thesis[remove]4

Rights Statement

In Copyright4

Subject

Broadband communication systems1
Supervised learning (Machine learning)1

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