Context-Aware Handover Policies in HetNets

Next generation cellular systems are expected to entail a wide variety of wireless coverage zones, with cells of different sizes and capacities that can overlap in space and share the transmission resources. In this scenario, which is referred to as Heterogeneous Networks (HetNets), a fundamental challenge is the management of the handover process between macro, femto and pico cells. To limit the number of handovers and the signaling between the cells, it will hence be crucial to manage the user's mobility considering the context parameters, such as cells size, traffic loads, and user velocity. In this paper, we propose a theoretical model to characterize the performance of a mobile user in a HetNet scenario as a function of the user's mobility, the power profile of the neighboring cells, the handover parameters, and the traffic load of the different cells. We propose a Markov-based framework to model the handover process for the mobile user, and derive an optimal context-dependent handover criterion. The mathematical model is validated by means of simulations, comparing the performance of our strategy with conventional handover optimization techniques in different scenarios. Finally, we show the impact of the handover regulation on the users performance and how it is possible to improve the users capacity exploiting context information

Performance Comparison of Dual Connectivity and Hard Handover for LTE-5G Tight Integration in mmWave Cellular Networks

MmWave communications are expected to play a major role in the Fifth generation of mobile networks. They offer a potential multi-gigabit throughput and an ultra-low radio latency, but at the same time suffer from high isotropic pathloss, and a coverage area much smaller than the one of LTE macrocells. In order to address these issues, highly directional beamforming and a very high-density deployment of mmWave base stations were proposed. This Thesis aims to improve the reliability and performance of the 5G network by studying its tight and seamless integration with the current LTE cellular network. In particular, the LTE base stations can provide a coverage layer for 5G mobile terminals, because they operate on microWave frequencies, which are less sensitive to blockage and have a lower pathloss. This document is a copy of the Master's Thesis carried out by Mr. Michele Polese under the supervision of Dr. Marco Mezzavilla and Prof. Michele Zorzi. It will propose an LTE-5G tight integration architecture, based on mobile terminals' dual connectivity to LTE and 5G radio access networks, and will evaluate which are the new network procedures that will be needed to support it. Moreover, this new architecture will be implemented in the ns-3 simulator, and a thorough simulation campaign will be conducted in order to evaluate its performance, with respect to the baseline of handover between LTE and 5G.Comment: Master's Thesis carried out by Mr. Michele Polese under the supervision of Dr. Marco Mezzavilla and Prof. Michele Zorz

Predicting a User's Next Cell With Supervised Learning Based on Channel States

Knowing a user's next cell allows more efficient resource allocation and enables new location-aware services. To anticipate the cell a user will hand-over to, we introduce a new machine learning based prediction system. Therein, we formulate the prediction as a classification problem based on information that is readily available in cellular networks. Using only Channel State Information (CSI) and handover history, we perform classification by embedding Support Vector Machines (SVMs) into an efficient pre-processing structure. Simulation results from a Manhattan Grid scenario and from a realistic radio map of downtown Frankfurt show that our system provides timely prediction at high accuracy.Comment: The 14th IEEE International Workshop on Signal Processing Advances for Wireless Communications (SPAWC), Darmstadt : Germany (2013