Analysis of LTE-A Heterogeneous Networks with SIR-based Cell Association and Stochastic Geometry

This paper provides an analytical framework to characterize the performance of Heterogeneous Networks (HetNets), where the positions of base stations and users are modeled by spatial Poisson Point Processes (stochastic geometry). We have been able to formally derive outage probability, rate coverage probability, and mean user bit-rate when a frequency reuse of $K$ and a novel prioritized SIR-based cell association scheme are applied. A simulation approach has been adopted in order to validate our analytical model; theoretical results are in good agreement with simulation ones. The results obtained highlight that the adopted cell association technique allows very low outage probability and the fulfillment of certain bit-rate requirements by means of adequate selection of reuse factor and micro cell density. This analytical model can be adopted by network operators to gain insights on cell planning. Finally, the performance of our SIR-based cell association scheme has been validated through comparisons with other schemes in literature.Comment: Paper accepted to appear on the Journal of Communication Networks (accepted on November 28, 2017); 15 page

Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis

Database theory and database practice are typically the domain of computer scientists who adopt what may be termed an algorithmic perspective on their data. This perspective is very different than the more statistical perspective adopted by statisticians, scientific computers, machine learners, and other who work on what may be broadly termed statistical data analysis. In this article, I will address fundamental aspects of this algorithmic-statistical disconnect, with an eye to bridging the gap between these two very different approaches. A concept that lies at the heart of this disconnect is that of statistical regularization, a notion that has to do with how robust is the output of an algorithm to the noise properties of the input data. Although it is nearly completely absent from computer science, which historically has taken the input data as given and modeled algorithms discretely, regularization in one form or another is central to nearly every application domain that applies algorithms to noisy data. By using several case studies, I will illustrate, both theoretically and empirically, the nonobvious fact that approximate computation, in and of itself, can implicitly lead to statistical regularization. This and other recent work suggests that, by exploiting in a more principled way the statistical properties implicit in worst-case algorithms, one can in many cases satisfy the bicriteria of having algorithms that are scalable to very large-scale databases and that also have good inferential or predictive properties.Comment: To appear in the Proceedings of the 2012 ACM Symposium on Principles of Database Systems (PODS 2012