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Radio-based context awareness applications have drawn researchers' attention instead, because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for each radio-based application. Furthermore, they use one additional step to extract features before conducting classification and exploit deep learning as a classification tool. Although this feature extraction step helps explore patterns of raw signals, it generates unnecessary noise and information loss. The use of raw CSI signal without initial data processing was, however, considered as no usable patterns. In this article, we are the first to propose an innovative deep learning\u2013based general framework for both signal processing and classification. The key novelty of this article is that the framework can be generalised for all the radio-based context awareness applications with the use of raw CSI. We also eliminate the extra work to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.<\/jats:p>","DOI":"10.1145\/3467980","type":"journal-article","created":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T21:35:32Z","timestamp":1629149732000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["No Need of Data Pre-processing"],"prefix":"10.1145","volume":"2","author":[{"given":"Bo","family":"Wei","sequence":"first","affiliation":[{"name":"Northumbria University, United Kingdom and Wear, UK"}]},{"given":"Kai","family":"Li","sequence":"additional","affiliation":[{"name":"Research Centre in Real-Time and Embedded Computing System (CISTER), Porto, Portugal"}]},{"given":"Chengwen","family":"Luo","sequence":"additional","affiliation":[{"name":"Shenzhen University, Shenzhen, China"}]},{"given":"Weitao","family":"Xu","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Kowloon Tong, Hong Kong, China"}]},{"given":"Jin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen University, Shenzhen, China"}]},{"given":"Kuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Nebraska-Lincoln, NE, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,8,16]]},"reference":[{"volume-title":"NSDI'14","author":"Adib Fadel","key":"e_1_2_1_1_1","unstructured":"Fadel Adib , Zach Kabelac , Dina Katabi , and Robert C. 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