It is a promising vision to utilize white spaces, i.e., vacant VHF and UHF TV channels, to satisfy skyrocketing wireless data demand in both outdoor and indoor scenarios. While most prior works have focused on exploring outdoor white spaces, the indoor story is largely open for investigation. Motivated by this observation and that 70% of the spectrum demand comes from indoor environments, we carry out a comprehensive study of exploring indoor white spaces. We \ufb01rst present a large-scale measurement of outdoor and indoor TVspectrum occupancy in 30+ diverse locations in a typical metropolis Hong Kong. Our measurement results con\ufb01rm abundant white spaces available for exploration in a wide range of areas in metropolises. In particular, more than 50% and 70% of the TV spectrum are white spaces in outdoor and indoor scenarios, respectively. While there are substantially more white spaces in indoor scenarios than in outdoor scenarios, there is no effective solution for identifying indoor white spaces. To \ufb01ll in this gap, we propose the \ufb01rst system WISER (for White-space Indoor Spectrum EnhanceR), to identify and track indoor white spaces in a building, without requiring user devices to sense the spectrum. We discuss the design space of such system and justify our design choices using intensive real-world measurements. We design the architecture and algorithms to address the inherent challenges. We build a WISER prototype and carry out real-world experiments to evaluate its performance. Our results show that WISER can identify 30%-50% more indoor white spaces with negligible false alarms, as compared to alternative baseline approaches.<\/p>\n","protected":false},"excerpt":{"rendered":"
It is a promising vision to utilize white spaces, i.e., vacant VHF and UHF TV channels, to satisfy skyrocketing wireless data demand in both outdoor and indoor scenarios. While most prior works have focused on exploring outdoor white spaces, the indoor story is largely open for investigation. Motivated by this observation and that 70% of […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-165017","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"ACM MobiCom","msr_affiliation":"","msr_published_date":"2013-10-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"205233","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"WISER.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/WISER.pdf","id":205233,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":205233,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/WISER.pdf"}],"msr-author-ordering":[{"type":"text","value":"Guanglin Zhang","user_id":0,"rest_url":false},{"type":"text","value":"Lichao Yan","user_id":0,"rest_url":false},{"type":"text","value":"Jincheng Zhang","user_id":0,"rest_url":false},{"type":"text","value":"Xuhang Ying","user_id":0,"rest_url":false},{"type":"user_nicename","value":"b-mingch","user_id":31256,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=b-mingch"},{"type":"user_nicename","value":"ranveer","user_id":33344,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=ranveer"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[144899],"msr_project":[170158],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":170158,"post_title":"Networking Over White Spaces (KNOWS)","post_name":"networking-over-white-spaces-knows","post_type":"msr-project","post_date":"2008-12-19 20:10:09","post_modified":"2021-04-12 22:35:02","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/networking-over-white-spaces-knows\/","post_excerpt":"The next generation of wireless networks will include software defined radios, cognitive radios, and multi-radio systems which will co-exist harmoniously while operating over a very wide range of frequencies. Under the umbrella of the KNOWS project we are revisiting \"classical\" wireless networking problems and designing new solutions that incorporate and build upon recent advances in software and hardware technologies for networking over the recently opened white spaces spectrum. 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