Prediction assisted fast handovers for seamless IP mobility

Word processed copy.Includes bibliographical references (leaves 94-98).This research investigates the techniques used to improve the standard Mobile IP handover process and provide proactivity in network mobility management. Numerous fast handover proposals in the literature have recently adopted a cross-layer approach to enhance movement detection functionality and make terminal mobility more seamless. Such fast handover protocols are dependent on an anticipated link-layer trigger or pre-trigger to perform pre-handover service establishment operations. This research identifies the practical difficulties involved in implementing this type of trigger and proposes an alternative solution that integrates the concept of mobility prediction into a reactive fast handover scheme

UEFA-M: Utility-based energy efficient adaptive multimedia mechanism over LTE HetNet small cells

The emerging advances in mobile computing devices enable the adoption of new services like video over LTE (ViLTE), augmented and virtual reality, omnidirectional video, etc. However, these new services cannot be technologically achievable within the current networks without a rethink in the network architecture. A simple increase in system capacity will not be enough without considering the provisioning of Quality of Experience (QoE) as the basis for network control, customer loyalty and retention rate and thus increase in network operators’ revenue. This paper proposes an Utility-based Energy eFficient Adaptive Multimedia Mechanism (UEFA-M) over the LTE HetNet Small Cells environment that combines the use of utility theory and the concept of proactive handover to enable the adaptation of the multimedia stream ahead of the handover process in order to provide a seamless QoE to the mobile user and energy savings for their mobile device. Mathematical models for energy and quality are derived from previous real experimental data and integrated in the adaptation mechanism using the utility theory. The performance of the proposed adaptive multimedia scheme is analyzed and compared against a non-adaptive solution in terms of energy efficiency and Mean Opinion Score (MOS

Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks

This study demonstrates the feasibility of the proactive received power prediction by leveraging spatiotemporal visual sensing information toward the reliable millimeter-wave (mmWave) networks. Since the received power on a mmWave link can attenuate aperiodically due to a human blockage, the long-term series of the future received power cannot be predicted by analyzing the received signals before the blockage occurs. We propose a novel mechanism that predicts a time series of the received power from the next moment to even several hundred milliseconds ahead. The key idea is to leverage the camera imagery and machine learning (ML). The time-sequential images can involve the spatial geometry and the mobility of obstacles representing the mmWave signal propagation. ML is used to build the prediction model from the dataset of sequential images labeled with the received power in several hundred milliseconds ahead of when each image is obtained. The simulation and experimental evaluations using IEEE 802.11ad devices and a depth camera show that the proposed mechanism employing convolutional LSTM predicted a time series of the received power in up to 500 ms ahead at an inference time of less than 3 ms with a root-mean-square error of 3.5 dB