{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-learning-in-mobile-and-wireless","title":"Deep Learning in Mobile and Wireless Networking: A Survey","arxiv_id":"1803.04311","date":"2018-03-12","proceeding":null,"authors":["Chaoyun Zhang","Paul Patras","Hamed Haddadi"],"abstract":"The rapid uptake of mobile devices and the rising popularity of mobile\napplications and services pose unprecedented demands on mobile and wireless\nnetworking infrastructure. Upcoming 5G systems are evolving to support\nexploding mobile traffic volumes, agile management of network resource to\nmaximize user experience, and extraction of fine-grained real-time analytics.\nFulfilling these tasks is challenging, as mobile environments are increasingly\ncomplex, heterogeneous, and evolving. One potential solution is to resort to\nadvanced machine learning techniques to help managing the rise in data volumes\nand algorithm-driven applications. The recent success of deep learning\nunderpins new and powerful tools that tackle problems in this space.\n  In this paper we bridge the gap between deep learning and mobile and wireless\nnetworking research, by presenting a comprehensive survey of the crossovers\nbetween the two areas. We first briefly introduce essential background and\nstate-of-the-art in deep learning techniques with potential applications to\nnetworking. We then discuss several techniques and platforms that facilitate\nthe efficient deployment of deep learning onto mobile systems. Subsequently, we\nprovide an encyclopedic review of mobile and wireless networking research based\non deep learning, which we categorize by different domains. Drawing from our\nexperience, we discuss how to tailor deep learning to mobile environments. We\ncomplete this survey by pinpointing current challenges and open future\ndirections for research.","url_abs":"http://arxiv.org/abs/1803.04311v3","url_pdf":"http://arxiv.org/pdf/1803.04311v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"management","task_name":"Management"},{"task_slug":"survey","task_name":"Survey"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-sins","task":"Link Prediction","dataset":"SINS","model":"mlp","rank_in_archive_order":1,"of":1,"metrics":{"Scaled time-delay embeddings":"213"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.04311","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}