An approach using support vector regression for mobile location in cellular networks

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• 作者：Robson D.A. Timoteo^a ; ^{rdat@cin.ufpe.br" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Lizandro N. Silva¹ ; ^b ; ^{lizandro.silva@telefonica.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Daniel C. Cunha ; ^a ; ^{dcunha@cin.ufpe.br" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; George D.C. Cavalcanti^a ; ^{gdcc@cin.ufpe.br" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Wireless communications ; Positioning system ; Fingerprinting techniques ; Machine learning ; Support vector regression
• 刊名：Computer Networks
• 出版年：2016
• 出版时间：11 February 2016
• 年：2016
• 卷：95
• 期：Complete
• 页码：51-61
• 全文大小：2692 K

文摘

Wireless positioning systems have become very popular in recent years. One of the reasons is the fact that the use of a new paradigm named Internet of Things has been increasing in the scenario of wireless communications. Since a high demand for accurate positioning in wireless networks has become more intensive, especially for location-based services, the investigation of mobile positioning using radiolocalization techniques is an open research problem. Based on this context, we propose a fingerprinting approach using support vector regression to estimate the position of a mobile terminal in cellular networks. Simulation results indicate the proposed technique has a lower error distance prediction and is less sensitive to a Rayleigh distributed noise than the fingerprinting techniques based on COST-231 and ECC-33 propagation models.