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Characterization of Intended and Unintended RF Emissions
Characterization of Intended and Unintended RF Emissions
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211150946
- ISBN
- 9798382199832
- DDC
- 621.3
- 서명/저자
- Characterization of Intended and Unintended RF Emissions
- 발행사항
- [Sl] : University of California, San Diego, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 140 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
- 주기사항
- Advisor: Gerstoft, Peter.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2024.
- 초록/해제
- 요약Spectrum sensing is essential for enabling optimal spectrum usage and ensuring data security, especially with the proliferation of IoT devices. Spectrum sensing involves detecting and characterizing intentional RF emissions called overt, and unintentional RF emissions called emanations. The thesis focuses on two pivotal aspects of spectrum sensing: characterization of overt specifically modulation classification and characterization of emanations.Three distinct works are presented on modulation classification. DL has been successfully used recently. The focus, however, has been model-centric, with attempts to improve performance on the standard synthetic dataset RML16. The quality of the training dataset impacts model performance on real data. A hybrid approach is taken by leveraging wireless domain knowledge to improve dataset quality. The first two works respectively leverage domain knowledge of wireless channel conditions and SNR to improve dataset quality. Over-the-air (OTA) data captured using USRP radios are used in these works.In the first work, studies are done to understand the performance impact due to the disparity of probability distribution between training and test data within the context of channel conditions. This is studied for OTA data collected in channels emulating LOS, NLOS, and AWGN. In the second work, signal processing advances in blind SNR estimation are leveraged to improve DL performance on modulation classification. A training methodology is introduced that partitions OTA data into subsets of different SNR levels. For the third work, shortcomings such as errors and ad-hoc choice of parameters are identified in RML16. A new realistic benchmark dataset RML22 is provided with the errors corrected and the choice of parameters justified. Thorough mathematical derivations are provided for the wireless models used to generate data. Performance impact due to artifacts and model parameterization is studied using the RML22 data generation framework.For the second paradigm of detection and characterization of emanations, an HW agnostic solution is proposed. Prior work focused on profiling specific HW but scalability led to the need for a HW-agnostic solution. Emanations are detected by scanning for the signature of harmonics from leakages of clock signals. A signal processing algorithm is provided to remove artifacts and estimate the pitch of harmonics that characterizes the emanation. IQ data is collected from the source of emanations placed inside a sanitized shield room using Signal Hound SDR. Results for anomaly detection using emanation patterns are presented for the use cases compromising data security: damaged electronic peripherals, and illegal copying of data to external storage devices.
- 일반주제명
- Electrical engineering
- 일반주제명
- Computer engineering
- 일반주제명
- Systems science
- 키워드
- Spectrum sensing
- 키워드
- Emanations
- 키워드
- Clock signals
- 기타저자
- University of California, San Diego Electrical and Computer Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-10B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211150946
■006m o d
■007cr#unu||||||||
■020 ▼a9798382199832
■035 ▼a(MiAaPQ)AAI30992492
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.3
■1001 ▼aSathyanarayanan, Venkatesh.
■24510▼aCharacterization of Intended and Unintended RF Emissions
■260 ▼a[Sl]▼bUniversity of California, San Diego▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a140 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-10, Section: B.
■500 ▼aAdvisor: Gerstoft, Peter.
■5021 ▼aThesis (Ph.D.)--University of California, San Diego, 2024.
■520 ▼aSpectrum sensing is essential for enabling optimal spectrum usage and ensuring data security, especially with the proliferation of IoT devices. Spectrum sensing involves detecting and characterizing intentional RF emissions called overt, and unintentional RF emissions called emanations. The thesis focuses on two pivotal aspects of spectrum sensing: characterization of overt specifically modulation classification and characterization of emanations.Three distinct works are presented on modulation classification. DL has been successfully used recently. The focus, however, has been model-centric, with attempts to improve performance on the standard synthetic dataset RML16. The quality of the training dataset impacts model performance on real data. A hybrid approach is taken by leveraging wireless domain knowledge to improve dataset quality. The first two works respectively leverage domain knowledge of wireless channel conditions and SNR to improve dataset quality. Over-the-air (OTA) data captured using USRP radios are used in these works.In the first work, studies are done to understand the performance impact due to the disparity of probability distribution between training and test data within the context of channel conditions. This is studied for OTA data collected in channels emulating LOS, NLOS, and AWGN. In the second work, signal processing advances in blind SNR estimation are leveraged to improve DL performance on modulation classification. A training methodology is introduced that partitions OTA data into subsets of different SNR levels. For the third work, shortcomings such as errors and ad-hoc choice of parameters are identified in RML16. A new realistic benchmark dataset RML22 is provided with the errors corrected and the choice of parameters justified. Thorough mathematical derivations are provided for the wireless models used to generate data. Performance impact due to artifacts and model parameterization is studied using the RML22 data generation framework.For the second paradigm of detection and characterization of emanations, an HW agnostic solution is proposed. Prior work focused on profiling specific HW but scalability led to the need for a HW-agnostic solution. Emanations are detected by scanning for the signature of harmonics from leakages of clock signals. A signal processing algorithm is provided to remove artifacts and estimate the pitch of harmonics that characterizes the emanation. IQ data is collected from the source of emanations placed inside a sanitized shield room using Signal Hound SDR. Results for anomaly detection using emanation patterns are presented for the use cases compromising data security: damaged electronic peripherals, and illegal copying of data to external storage devices.
■590 ▼aSchool code: 0033.
■650 4▼aElectrical engineering
■650 4▼aComputer engineering
■650 4▼aSystems science
■653 ▼aSpectrum sensing
■653 ▼aEmanations
■653 ▼aClock signals
■653 ▼aSignal processing algorithm
■690 ▼a0544
■690 ▼a0464
■690 ▼a0790
■71020▼aUniversity of California, San Diego▼bElectrical and Computer Engineering.
■7730 ▼tDissertations Abstracts International▼g85-10B.
■790 ▼a0033
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160267▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
