{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/134948"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/134948","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Vision-Enhanced Communications: On the Benefits of NLOS/LOS Knowledge in Wireless Systems","abstract":"The proliferation of Internet of Things (IoT) devices equipped with meteorological, auditory, optical, and infrared sensors has opened the door to integrating sensor-based information into existing physical layer communication system design. Many properties of the wireless channel, especially for mobile applications, are highly dynamic and easily observable using non-radio frequency (RF) sensors or RF sensors operating out-of-band (OOB) which we refer to as vision sensors. Using vision sensors to provide information about one such property, that of line-of-sight (LOS)/non-line-ofsight (NLOS) states, is the central focus of this work. A generalized signal detection framework is presented for a vision sensor-aided receiver operating in a binary continuous-time Markov chain (CTMC) channel environment wherein the NLOS/LOS state toggles intermittently. Several cases are explored wherein varying degrees of NLOS/LOS knowledge are available at the receiver with an emphasis on labeled vs unlabeled information. Bayes risk and composite likelihood ratio test (LRT) methods are used to derive the optimal decision rule in both constant false-alarm rate (CFAR) and minimum probability of error (min(Pe)) paradigms. It is shown that a dynamic detection scheme utilizing labeled information, including imperfect error-prone labels, sourced from vision sensors can improve upon the uniformly-most-powerful (UMP) test in an ensemble of trials, yielding higher CFAR detection rates than static detectors without vision sensors. Further, it is shown that unlabeled information, while matching the CFAR performance of the UMP test, can yield a lower overall error rate compared to a blind receiver with no NLOS/LOS knowledge.","abstract_html":"The proliferation of Internet of Things (IoT) devices equipped with meteorological, auditory, optical, and infrared sensors has opened the door to integrating sensor-based information into existing physical layer communication system design. Many properties of the wireless channel, especially for mobile applications, are highly dynamic and easily observable using non-radio frequency (RF) sensors or RF sensors operating out-of-band (OOB) which we refer to as vision sensors. Using vision sensors to provide information about one such property, that of line-of-sight (LOS)/non-line-ofsight (NLOS) states, is the central focus of this work. A generalized signal detection framework is presented for a vision sensor-aided receiver operating in a binary continuous-time Markov chain (CTMC) channel environment wherein the NLOS/LOS state toggles intermittently. Several cases are explored wherein varying degrees of NLOS/LOS knowledge are available at the receiver with an emphasis on labeled vs unlabeled information. Bayes risk and composite likelihood ratio test (LRT) methods are used to derive the optimal decision rule in both constant false-alarm rate (CFAR) and minimum probability of error (min(Pe)) paradigms. It is shown that a dynamic detection scheme utilizing labeled information, including imperfect error-prone labels, sourced from vision sensors can improve upon the uniformly-most-powerful (UMP) test in an ensemble of trials, yielding higher CFAR detection rates than static detectors without vision sensors. Further, it is shown that unlabeled information, while matching the CFAR performance of the UMP test, can yield a lower overall error rate compared to a blind receiver with no NLOS/LOS knowledge.","abstract_has_math":false,"creators":["Brown, Samuel Benjamin"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Electrical Engineering","degree_department":"Electrical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Dhillon, Harpreet Singh"],"committee_members":["Headley, William C.","Buehrer, Richard M."],"year":2025,"date_issued":"2025-05-30","date_published":"2025-05-30","updated_at":"2026-07-22T22:19:51Z","subjects":["Sensors","Internet of Things","LOS/NLOS","Out-of-Band Knowledge","CFAR","CTMC","Markov Channel","Dynamic Channel","Composite Hypothesis Testing"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:43944"],"render_values":[{"text":"vt_gsexam:43944","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/134948","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Dhillon, Harpreet Singh"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Headley, William C.","Buehrer, Richard M."]},{"key":"dc:contributor.department","label":"Department","values":["Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Brown, Samuel Benjamin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-05-31T08:01:55Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-05-31T08:01:55Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05-30"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Sensors","Internet of Things","LOS/NLOS","Out-of-Band Knowledge","CFAR","CTMC","Markov Channel","Dynamic Channel","Composite Hypothesis Testing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:43944"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/134948"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The proliferation of Internet of Things (IoT) devices equipped with meteorological, auditory, optical, and infrared sensors has opened the door to integrating sensor-based information into existing physical layer communication system design. Many properties of the wireless channel, especially for mobile applications, are highly dynamic and easily observable using non-radio frequency (RF) sensors or RF sensors operating out-of-band (OOB) which we refer to as vision sensors. Using vision sensors to provide information about one such property, that of line-of-sight (LOS)/non-line-ofsight (NLOS) states, is the central focus of this work. A generalized signal detection framework is presented for a vision sensor-aided receiver operating in a binary continuous-time Markov chain (CTMC) channel environment wherein the NLOS/LOS state toggles intermittently. Several cases are explored wherein varying degrees of NLOS/LOS knowledge are available at the receiver with an emphasis on labeled vs unlabeled information. Bayes risk and composite likelihood ratio test (LRT) methods are used to derive the optimal decision rule in both constant false-alarm rate (CFAR) and minimum probability of error (min(Pe)) paradigms. It is shown that a dynamic detection scheme utilizing labeled information, including imperfect error-prone labels, sourced from vision sensors can improve upon the uniformly-most-powerful (UMP) test in an ensemble of trials, yielding higher CFAR detection rates than static detectors without vision sensors. Further, it is shown that unlabeled information, while matching the CFAR performance of the UMP test, can yield a lower overall error rate compared to a blind receiver with no NLOS/LOS knowledge."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["The proliferation of Internet of Things (IoT) devices equipped with meteorological, auditory, optical, and infrared sensors has opened the door to integrating sensor-based information into existing wireless communication system design. Many properties of the radio frequency (RF) channel, especially for mobile applications, are highly dynamic and easily observable using sensors operating out-of-band (OOB) such as optical and infrared (IR) cameras and radars operating in different frequency ranges. These vision sensors can provide information about the physical propagation environment beyond what is typically sensed with an existing RF receiver. This work explores the benefits of using vision sensors to determine whether a communication link is line-of-sight (LOS) with a direct path to the transmitter or non-line-of-sight (NLOS) without a direct path. A performance analysis is presented for a receiver with vision information using classical hypothesis testing and detection theory, and a generalized framework is presented to compare performance with perfect vision information and imperfect error-prone information. It is shown that using a dynamic detector, which can adjust its sensitivity based on information about the NLOS/LOS state, can improve its detection performance over a static detector which lacks vision information. This result demonstrates that wireless communications systems can indeed benefit from vision sensors, even if the information they provide is partially corrupted and not always available."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Vision-Enhanced Communications: On the Benefits of NLOS/LOS Knowledge in Wireless Systems"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Dhillon, Harpreet Singh"],"dc:contributor.committeemember":["Headley, William C.","Buehrer, Richard M."],"dc:contributor.department":["Electrical Engineering"],"dc:creator":["Brown, Samuel Benjamin"],"dc:date.accessioned":["2025-05-31T08:01:55Z"],"dc:date.available":["2025-05-31T08:01:55Z"],"dc:date.issued":["2025-05-30"],"dc:description.abstract":["The proliferation of Internet of Things (IoT) devices equipped with meteorological, auditory, optical, and infrared sensors has opened the door to integrating sensor-based information into existing physical layer communication system design. Many properties of the wireless channel, especially for mobile applications, are highly dynamic and easily observable using non-radio frequency (RF) sensors or RF sensors operating out-of-band (OOB) which we refer to as vision sensors. Using vision sensors to provide information about one such property, that of line-of-sight (LOS)/non-line-ofsight (NLOS) states, is the central focus of this work. A generalized signal detection framework is presented for a vision sensor-aided receiver operating in a binary continuous-time Markov chain (CTMC) channel environment wherein the NLOS/LOS state toggles intermittently. Several cases are explored wherein varying degrees of NLOS/LOS knowledge are available at the receiver with an emphasis on labeled vs unlabeled information. Bayes risk and composite likelihood ratio test (LRT) methods are used to derive the optimal decision rule in both constant false-alarm rate (CFAR) and minimum probability of error (min(Pe)) paradigms. It is shown that a dynamic detection scheme utilizing labeled information, including imperfect error-prone labels, sourced from vision sensors can improve upon the uniformly-most-powerful (UMP) test in an ensemble of trials, yielding higher CFAR detection rates than static detectors without vision sensors. Further, it is shown that unlabeled information, while matching the CFAR performance of the UMP test, can yield a lower overall error rate compared to a blind receiver with no NLOS/LOS knowledge."],"dc:description.abstractgeneral":["The proliferation of Internet of Things (IoT) devices equipped with meteorological, auditory, optical, and infrared sensors has opened the door to integrating sensor-based information into existing wireless communication system design. Many properties of the radio frequency (RF) channel, especially for mobile applications, are highly dynamic and easily observable using sensors operating out-of-band (OOB) such as optical and infrared (IR) cameras and radars operating in different frequency ranges. These vision sensors can provide information about the physical propagation environment beyond what is typically sensed with an existing RF receiver. This work explores the benefits of using vision sensors to determine whether a communication link is line-of-sight (LOS) with a direct path to the transmitter or non-line-of-sight (NLOS) without a direct path. A performance analysis is presented for a receiver with vision information using classical hypothesis testing and detection theory, and a generalized framework is presented to compare performance with perfect vision information and imperfect error-prone information. It is shown that using a dynamic detector, which can adjust its sensitivity based on information about the NLOS/LOS state, can improve its detection performance over a static detector which lacks vision information. This result demonstrates that wireless communications systems can indeed benefit from vision sensors, even if the information they provide is partially corrupted and not always available."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:43944"],"dc:identifier.uri":["https://hdl.handle.net/10919/134948"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Sensors","Internet of Things","LOS/NLOS","Out-of-Band Knowledge","CFAR","CTMC","Markov Channel","Dynamic Channel","Composite Hypothesis Testing"],"dc:title":["Vision-Enhanced Communications: On the Benefits of NLOS/LOS Knowledge in Wireless Systems"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:51Z"}