This paper uses big data technologies to study base stations' behaviors and activities and
their predictability in mobile cellular networks. With new technologies quickly appearing …

In this paper, a novel machine learning (ML) framework is proposed for enabling a
predictive, efficient deployment of unmanned aerial vehicles (UAVs), acting as aerial base …

Prior works in a designing caching policy do not distinguish content popularity with user
preference. In this paper, we illustrate the caching gain by exploiting individual user …

Anticipatory networking is a recent branch of network optimization based on prediction of the
system state. Our work specifically tackles prediction-driven resource allocation for mobile …

In this work we design and implement a Neural Network that can identify recurrent patterns
in various metrics which can be then used for cellular network traffic forecasting. Based on a …

The existing proactive caching policies are designed by assuming that all users request
contents with identical activity level at uniformly distributed or known locations, among which …

In large-scale mobile networks, energy minimization problem pertaining to base station (BS)
switching is known to require high computational complexity. In this paper, we propose a …

Abstract Call Detail Records (CDR) are an important source of information in the study of
diverse aspects of human mobility. The accuracy of mobility information granted by CDR …

Due to the increasing demand of wireless communication, the number of radio base stations
has been growing excessively. The wireless network is designed for maximum traffic load …

In this paper, we propose a novel method for low-latency 10-Gigabit-capable symmetric
passive optical network (XGS-PON) mobile front-haul for small cell cloud radio access …