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In this paper, we jointly optimize content placement and user association to minimize the average content delivery delay in cache\u2010enabled HCNs based on flow\u2010level models. This formulation considers (1) different timescales of content placement and content delivery, (2) locality of content popularity, and (3) the heterogeneity of spatial traffic distribution, which are often neglected in existing researches. The joint optimization problem is formulated as a mixed integer nonlinear programming problem in load\u2010non\u2010coupled and load\u2010coupled models, respectively. We decouple this problem into two interrelated subproblems and resolve them individually. For the user association problem under a given content placement situation, we propose a content\u2010level selective association algorithm, which allows the requests for different contents at the same location to connect to different base stations (BSs). In addition, we propose a greedy content caching algorithm to add contents to the caches of BSs in an iterative manner. These two algorithms are alternately executed until the caches of all the BSs are filled to capacity. Simulation results show that the proposed algorithm achieves better performance in terms of average delay and backhaul usage compared with traditional content placement and user association approaches.<\/jats:p>","DOI":"10.1155\/2018\/1628283","type":"journal-article","created":{"date-parts":[[2018,11,19]],"date-time":"2018-11-19T23:30:41Z","timestamp":1542670241000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint Optimization of Content Placement and User Association in Cache\u2010Enabled Heterogeneous Cellular Networks Based on Flow\u2010Level Models"],"prefix":"10.1155","volume":"2018","author":[{"given":"Hua","family":"Qu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0444-0607","authenticated-orcid":false,"given":"Gongye","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenjie","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8732-9786","authenticated-orcid":false,"given":"Shuyuan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,11,19]]},"reference":[{"key":"e_1_2_12_1_2","unstructured":"Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update 2016\u20132021 San Jose CA USA 2017."},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/mcom.2012.6211486"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-015-3045-3"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2017.2680958"},{"key":"e_1_2_12_5_2","doi-asserted-by":"crossref","unstructured":"YuH. 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