D2D communications in 5G mobile cellular networks : we propose and validate a novel approach to mobility management

University of Technology Sydney. Faculty of Engineering and Information Technology.Fifth Generation (5G) stands for future fitness combined with flexible technical solutions that combine with the latest wireless technology. 5G is expected to multiply a thousand times (1000x) in data speed with 20.4 billion devices (IoT) connected to the network by 2020. This literally means everything connecting to everything. From the network point of view, lower latency along with high flexibility is not limited just to 5G. It is already being implemented in real networks. The number of wireless devices connected to networks has increased remarkably over the last couple of decades. Ubiquitous voice and data connections are the fundamental requirements for the next generation of wireless technology. Device-to-Device communication is widely known as D2D. It is a new paradigm for cellular communication. It was initially proposed to boost network performance. It is considered to be an integral part of the next generation (5G) of telecommunications networks. It takes place when two devices communicate directly without significant help from the base station. In a cellular network, Device-to-Device communication has been viewed as a promising technology overcoming many existing problems. These include capacity, quality and scarce spectrum resources. However, this comes at the price of increased interference and complex mobility issues, even though it was proposed as a new paradigm to enhance network performance. Nevertheless, it is still a challenge to manage devices that are moving. Cellular devices without well-managed mobility are hardly acceptable. Considering in-band underlay D2D communication, a well-managed mobility system in cellular communication should have lower latency, lower power consumption and higher data rates. In this dissertation, we review existing mobility management systems for LTE-Advanced technology and propose an algorithm to be used over the current system so that lower signalling overheads and less delay, along with uninterrupted D2D communication, are guaranteed. We model and simulate our algorithm, comparing the results with mathematical models based on Markov theory. As in other similar communication systems, mobility management for D2D communication is yet to be explored fully. There are few research papers published so far. What we can say is that the intention of such systems in cellular networks are to enable lower latency, lower power consumption, less complexity and, last but not least, uninterrupted data connections. Our simulation results validate our proposed model and highlight D2D communication and its mobility issues. An essential element of our proposal is to estimate the user’s location. We can say that a mobility management system for D2D communication is hardly workable if the location of the users is not realisable. This dissertation also shows some latest techniques for estimating the direction of arrival (DOA) with mathematical models and simulation results. Smart antenna systems are proposed. It is possible to determine the location of a user by considering the uplink transmission system. Estimating the channel and actual path delay is also an important task, which might be done by using 1D uniform linear array (ULA) or 2D Uniform Rectangular (URA) array antenna systems. In this chapter, 1D ULA is described utilising some well-known techniques. The channel characteristics largely determine the performance of an end-to-end communication system. It determines the signal transformation while propagating through the channel between receivers and transmitters. Accurate channel information is crucial for both the transmitter and receiver ends to perform at their best. The ultimate focus of this part is to estimate the channel based on 2D parameter estimation. Uniform Rectangular Array (URA) is used to perform the 2D parameter estimation. It is possible to estimate azimuth and elevation of a source by using the URA model. The problem of mobility in this context has been investigated in few papers, with no reliable solutions as yet. We propose a unique algorithm for mobility management for D2D communications. In this dissertation, we highlight and explain the mobility model mathematically and analytically, along with the simulation of the Markovian model. A Markov model is essentially a simplified approach to describing a system that occupies a discrete state at any point in time. We also make a bridge between our mobility algorithm and a Markovian model

Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks.

This thesis reports on the design and validation of estimation and planning algorithms for underwater vehicle cooperative localization. While attitude and depth are easily instrumented with bounded-error, autonomous underwater vehicles (AUVs) have no internal sensor that directly observes XY position. The global positioning system (GPS) and other radio-based navigation techniques are not available because of the strong attenuation of electromagnetic signals in seawater. The navigation algorithms presented herein fuse local body-frame rate and attitude measurements with range observations between vehicles within a decentralized architecture. The acoustic communication channel is both unreliable and low bandwidth, precluding many state-of-the-art terrestrial cooperative navigation algorithms. We exploit the underlying structure of a post-process centralized estimator in order to derive two real-time decentralized estimation frameworks. First, the origin state method enables a client vehicle to exactly reproduce the corresponding centralized estimate within a server-to-client vehicle network. Second, a graph-based navigation framework produces an approximate reconstruction of the centralized estimate onboard each vehicle. Finally, we present a method to plan a locally optimal server path to localize a client vehicle along a desired nominal trajectory. The planning algorithm introduces a probabilistic channel model into prior Gaussian belief space planning frameworks. In summary, cooperative localization reduces XY position error growth within underwater vehicle networks. Moreover, these methods remove the reliance on static beacon networks, which do not scale to large vehicle networks and limit the range of operations. Each proposed localization algorithm was validated in full-scale AUV field trials. The planning framework was evaluated through numerical simulation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113428/1/jmwalls_1.pd

A comprehensive approach to MPSoC security: achieving network-on-chip security : a hierarchical, multi-agent approach

Multiprocessor Systems-on-Chip (MPSoCs) are pervading our lives, acquiring ever increasing relevance in a large number of applications, including even safety-critical ones. MPSoCs, are becoming increasingly complex and heterogeneous; the Networks on Chip (NoC paradigm has been introduced to support scalable on-chip communication, and (in some cases) even with reconfigurability support. The increased complexity as well as the networking approach in turn make security aspects more critical. In this work we propose and implement a hierarchical multi-agent approach providing solutions to secure NoC based MPSoCs at different levels of design. We develop a flexible, scalable and modular structure that integrates protection of different elements in the MPSoC (e.g. memory, processors) from different attack scenarios. Rather than focusing on protection strategies specifically devised for an individual attack or a particular core, this work aims at providing a comprehensive, system-level protection strategy: this constitutes its main methodological contribution. We prove feasibility of the concepts via prototype realization in FPGA technology

Multilevel Delayed Acceptance MCMC with Applications to Hydrogeological Inverse Problems

Quantifying the uncertainty of model predictions is a critical task for engineering decision support systems. This is a particularly challenging effort in the context of statistical inverse problems, where the model parameters are unknown or poorly constrained, and where the data is often scarce. Many such problems emerge in the fields of hydrology and hydro--environmental engineering in general, and in hydrogeology in particular. While methods for rigorously quantifying the uncertainty of such problems exist, they are often prohibitively computationally expensive, particularly when the forward model is high--dimensional and expensive to evaluate. In this thesis, I present a Metropolis--Hastings algorithm, namely the Multilevel Delayed Acceptance (MLDA) algorithm, which exploits a hierarchy of forward models of increasing computational cost to significantly reduce the total cost of quantifying the uncertainty of high--dimensional, expensive forward models. The algorithm is shown to be in detailed balance with the posterior distribution of parameters, and the computational gains of the algorithm is demonstrated on multiple examples. Additionally, I present an approach for exploiting a deep neural network as an ultra--fast model approximation in an MLDA model hierarchy. This method is demonstrated in the context of both 2D and 3D groundwater flow modelling. Finally, I present a novel approach to adaptive optimal design of groundwater surveying, in which MLDA is employed to construct the posterior Monte Carlo estimates. This method utilises the posterior uncertainty of the primary problem in conjunction with the expected solution to an adjoint problem to sequentially determine the optimal location of the next datapoint.Engineering and Physical Sciences Research Council (EPSRC)Alan Turing InstituteEngineering and Physical Sciences Research Council (EPSRC

Selectively decentralized reinforcement learning

Indiana University-Purdue University Indianapolis (IUPUI)The main contributions in this thesis include the selectively decentralized method in solving multi-agent reinforcement learning problems and the discretized Markov-decision-process (MDP) algorithm to compute the sub-optimal learning policy in completely unknown learning and control problems. These contributions tackle several challenges in multi-agent reinforcement learning: the unknown and dynamic nature of the learning environment, the difficulty in computing the closed-form solution of the learning problem, the slow learning performance in large-scale systems, and the questions of how/when/to whom the learning agents should communicate among themselves. Through this thesis, the selectively decentralized method, which evaluates all of the possible communicative strategies, not only increases the learning speed, achieves better learning goals but also could learn the communicative policy for each learning agent. Compared to the other state-of-the-art approaches, this thesis’s contributions offer two advantages. First, the selectively decentralized method could incorporate a wide range of well-known algorithms, including the discretized MDP, in single-agent reinforcement learning; meanwhile, the state-of-the-art approaches usually could be applied for one class of algorithms. Second, the discretized MDP algorithm could compute the sub-optimal learning policy when the environment is described in general nonlinear format; meanwhile, the other state-of-the-art approaches often assume that the environment is in limited format, particularly in feedback-linearization form. This thesis also discusses several alternative approaches for multi-agent learning, including Multidisciplinary Optimization. In addition, this thesis shows how the selectively decentralized method could successfully solve several real-worlds problems, particularly in mechanical and biological systems

Local Networks to Compete in the Global Era. The Italian SMEs Experience

This study is concerned with the factors that influence the cooperation among cluster-based firms. Theorists have consistently demonstrated the role and importance of economic externalities, such as knowledge spillovers, within industrial clusters. Less attention has been paid to the investigation of social based externalities, though it has been suggested that these may also accrue from geographical agglomeration. This study explores the development of cooperation between firms operating in a single industry sector and in close proximity. The results suggest that social networking has a greater influence than geographic proximity in facilitating inter-firm co-operation. A semi-structured questionnaire has been developed and the answers were analysed with a stepwise regression model.Networks, Inter-Firm Cooperation, SMEs