att_abstract={{Cellular network providers collect and use a wide variety of data for assessing the service quality experienced by their smartphone users. The data is essential for tasks ranging from event detection, problem diagnosis, impact analysis, coverage and capacity planning, load balancing, and performance optimization. For example, service quality measurements and data from drive-by tests provide useful and detailed information about different aspects of quality of service such as dropped calls due to handovers or radio interference. However, a major challenge for effective service quality management in operational setup is the presence of missing or unavailable data. Furthermore, the cellular data is inherently multidimensional, i.e. is a function of several variables such as location, device type, and time. Motivated by recent advances in handling multidimensional data, we propose to use tensor algebraic models and methods for cellular data prediction. The main idea is to model the data as a low rank tensor and use a rank constrained interpolation for data prediction. In this direction, we focus on two recently proposed algebraic models employing two different notions of tensor rank. We test and compare the performance of the two approaches on real data sets and indicate the regimes in which one method is superior to the other. Based on these observations the proposed algorithm chooses the best of the two approaches using cross-validation.}},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in IFIP/IEEE CNSM{{, 2016-10-31}}
	author={Vaneet Aggarwal and Ajay Mahimkar and Hongyao Ma and Zemin Zhang and Shuchin Aeron and Walter Willinger},
	institution={{IFIP/IEEE CNSM}},
	title={{Inferring Smartphone Service Quality using Tensor Methods}},