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Distinguishing the best whitespaces among a large number of candidates is expensive in terms of energy and time and has yet to be fully studied in the literature. This paper presents a spectrum sensing framework based on channel usability patterns mined from actual experimental data to address this problem. In contrast to spectrum prediction techniques that simply regard a channel as idle or usable and that construct binary series over time, we model channel quality considering not only SNR but also the duration for which communication can be achieved a continuous manner. With this method, both the spectrum utility and sensing accuracy are greatly improved while also significantly decreasing the time overheads.<\/jats:p>","DOI":"10.3233\/jifs-179084","type":"journal-article","created":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T11:33:42Z","timestamp":1558092822000},"page":"275-282","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Channel usability pattern guided spectrum prediction and sensing"],"prefix":"10.1177","volume":"37","author":[{"given":"Tang","family":"Xiaogangr","sequence":"first","affiliation":[{"name":"Astronautics Engineering Universtiy, Huairou District, Beijing, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wang","family":"Sun\u2019an","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, Shaanxi, P.R. 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