This thesis identifies and addresses two barriers to the adoption of Long Term Evolution (LTE) Device-to-Device (D2D) communication enabled smart grid applications in out of core network coverage regions. The first barrier is the lack of accessible simulation software for engineers to develop and test the feasibility of their D2D LTE enabled smart grid application designs. The second barrier is the lack of a distributed resource allocation algorithm for LTE D2D communications that has been tailored to the needs of smart grid applications. A solution was proposed to the first barrier in the form of a simulator constructed in Matlab/Simulink used to simulate power systems and the underlying communication system, i.e., D2D communication protocol stack of Long Term Evolution (LTE). The simulator is built using Matlab's LTE System Toolbox, SimEvents, and Simscape Power Systems in addition to an in-house developed interface software to facilitate D2D communications in smart grid applications. To test the simulator, a simple fault location, isolation, and restoration (FLISR) application was implemented using the simulator to show that the LTE message timing is consistent with the relay signaling in the power system. A solution was proposed to the second barrier in the form of a multi-agent Q-learning based resource allocation algorithm that allows Long Term Evolution (LTE) enabled device-to-device (D2D) communication agents to generate orthogonal transmission schedules outside of network coverage. This algorithm reduces packet drop rates (PDR) in distributed D2D communication networks to meet the quality of service requirements of microgrid communications. The PDR and latency performance of the proposed algorithm was compared to the existing random self-allocation mechanism introduced under the Third Generation Partnership Project's LTE Release 12. The proposed algorithm outperformed the LTE algorithm for all tested scenarios, demonstrating 20-40% absolute reductions in PDR and 10-20 ms reductions in latency for all microgrid applications.

Cloud-Radio Access Network (C-RAN) est une architecture prometteuse pour faire face à l’augmentation exponentielle des demandes de trafic de données et surmonter les défis des réseaux de prochaine génération (5G). Le principe de base de CRAN consiste à diviser la station de base traditionnelle en deux entités : les unités de bande de base (BaseBand Unit, BBU) et les têtes radio distantes (Remote Radio Head, RRH) et à mettre en commun les BBUs de plusieurs stations dans des centres de données centralisés (pools de BBU). Ceci permet la réduction des coûts d’exploitation, l’amélioration de la capacité du réseau ainsi que des gains en termes d’utilisation des ressources. Pour atteindre ces objectifs, les opérateurs réseaux ont besoin d’investiguer de nouveaux algorithmes pour les problèmes d’allocation de ressources permettant ainsi de faciliter le déploiement de l’architecture C-RAN. La plupart de ces problèmes sont très complexes et donc très difficiles à résoudre. Par conséquent, nous utilisons l’optimisation combinatoire qui propose des outils puissants pour adresser ce type des problèmes.Un des principaux enjeux pour permettre le déploiement du C-RAN est de déterminer une affectation optimale des RRHs (antennes) aux centres de données centralisés (BBUs) en optimisant conjointement la latence sur le réseau de transmission fronthaul et la consommation des ressources. Nous modélisons ce problème à l’aide d’une formulation mathématique basée sur une approche de programmation linéaire en nombres entiers permettant de déterminer les stratégies optimales pour le problème d’affectation des ressources entre RRH-BBU et nous proposons également des heuristiques afin de pallier la difficulté au sens de la complexité algorithmique quand des instances larges du problème sont traitées, permettant ainsi le passage à l’échelle. Une affectation optimale des antennes aux BBUs réduit la latence de communication attendue et offre des gains en termes d’utilisation des ressources. Néanmoins, ces gains dépendent fortement de l’augmentation des niveaux d’interférence inter-cellulaire causés par la densité élevée des antennes déployées dans les réseaux C-RANs. Ainsi, nous proposons une formulation mathématique exacte basée sur les méthodes Branch-and-Cut qui consiste à consolider et ré-optimiser les rayons de couverture des antennes afin de minimiser les interférences inter-cellulaires et de garantir une couverture maximale du réseau conjointement. En plus de l’augmentation des niveaux d’interférence, la densité élevée des cellules dans le réseau CRAN augmente le nombre des fonctions BBUs ainsi que le trafic de données entre les antennes et les centres de données centralisés avec de fortes exigences en termes de latence sur le réseau fronthaul. Par conséquent, nous discutons dans la troisième partie de cette thèse comment placer d’une manière optimale les fonctions BBUs en considérant la solution split du 3GPP afin de trouver le meilleur compromis entre les avantages de la centralisation dans C-RAN et les forts besoins en latence et bande passante sur le réseau fronthaul. Nous proposons des algorithmes (exacts et heuristiques) issus de l’optimisation combinatoire afin de trouver rapidement des solutions optimales ou proches de l’optimum, même pour des instances larges du problèmes. / Cloud Radio Access Network (C-RAN) has been proposed as a promising architecture to meet the exponential growth in data traffic demands and to overcome the challenges of next generation mobile networks (5G). The main concept of C-RAN is to decouple the BaseBand Units (BBU) and the Remote Radio Heads (RRH), and place the BBUs in common edge data centers (BBU pools) for centralized processing. This gives a number of benefits in terms of cost savings, network capacity improvement and resource utilization gains. However, network operators need to investigate scalable and cost-efficient algorithms for resource allocation problems to enable and facilitate the deployment of C-RAN architecture. Most of these problems are very complex and thus very hard to solve. Hence, we use combinatorial optimization which provides powerful tools to efficiently address these problems.One of the key issues in the deployment of C-RAN is finding the optimal assignment of RRHs (or antennas) to edge data centers (BBUs) when jointly optimizing the fronthaul latency and resource consumption. We model this problem by a mathematical formulation based on an Integer Linear Programming (ILP) approach to provide the optimal strategies for the RRH-BBU assignment problem and we propose also low-complexity heuristic algorithms to rapidly reach good solutions for large problem instances. The optimal RRH-BBU assignment reduces the expected latency and offers resource utilization gains. Such gains can only be achieved when reducing the inter-cell interference caused by the dense deployment of cell sites. We propose an exact mathematical formulation based on Branch-and-Cut methods that enables to consolidate and re-optimize the antennas radii in order to jointly minimize inter-cell interference and guarantee a full network coverage in C-RAN. In addition to the increase of inter-cell interference, the high density of cells in C-RAN increases the amount of baseband processing as well as the amount of data traffic demands between antennas and centralized data centers when strong latency requirements on fronthaul network should be met. Therefore, we discuss in the third part of this thesis how to determine the optimal placement of BBU functions when considering 3GPP split option to find optimal tradeoffs between benefits of centralization in C-RAN and transport requirements. We propose exact and heuristic algorithms based on combinatorial optimization techniques to rapidly provide optimal or near-optimal solutions even for large network sizes.

Thesis advisor: Arthur Lewbel / The dissertation consists of three essays on different aspects of the collective household models in the household economics literature. The first essay estimates a collective household model for evaluating the Supplemental Nutrition Assistance Program (SNAP) among older households. I use longitudinal Homescan data to identify SNAP-eligible food. I find that husbands have relatively stronger preferences for food than wives, and that household demand is affected by bargaining power (i.e., control over resources) within households. Failure to account for this difference in preferences and control leads to underestimates of older couples' total food demand, and of their implied response (at both intensive and extensive margins) to a counterfactual experiment of replacing SNAP with a cash transfer program. I find that most eligible older households spend more on SNAP-eligible food than would be allowed by their SNAP benefits. Their spending patterns suggest that their poor diet is mainly due to low income rather than tastes. Overall these findings imply that a SNAP comparable cash transfer can be an effective tool to achieve the goals of the SNAP program. The second essay is joint work with my advisor Arthur Lewbel. We first prove identification of coefficients in a class of semiparametric models. We then apply these results to identify collective household consumption models. We extend the existing literature by proving point identification, rather than the weaker generic identification, of all the features of a collective household (including price effects). Moreover, we do so in a model where goods can be partly shared, and allowing children to have their own preferences, without observing child specific goods. We estimate the model using Japanese consumption data, where we find new results regarding the sharing and division of goods among husbands, wives, and children. The third essay is a joint paper with Tomoki Fujii. We study the intra-household inequality in resource allocation and bargaining within Japanese couples without children. We exploit a unique Japanese dataset in which individual private expenditures, savings, and time use information are available. From the data, we find that on average, the husband enjoys 1.5 times more purely private expenditures than the wife. However, the data only provides resource allocation on purely private expenditures, while 68 percent of household expenditures are devoted to the family, i.e., joint expenditures. We refer to the collective household literature in order to recover the unobserved sharing of total household expenditures, including both private and public goods. We find that the model-predicted sharing pattern is moderately consistent with the individual expenditure data. However, the intra-household inequality would be underestimated if we only use the sharing in purely private expenditures from the data. We find that Japanese wives are relatively disadvantaged to their husbands, no matter in purely private expenditures, total household expenditures, or gains from marriage. The findings in this paper provides certain external validity in terms of the collective household model of consumption, which we argue should be widely adopted in analyzing individual welfare in multi-person households. / Thesis (PhD) — Boston College, 2020. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

Multi-tier heterogeneous networks have become an essential constituent for next generation cellular networks. Meanwhile, energy efficiency (EE) has been considered a critical design criterion along with the traditional spectral efficiency (SE) metric. In this context, we study power and spectrum allocation for the recently proposed two-tier network architecture known as phantom cellular networks. The optimization framework includes both EE and SE. First, we consider sparsely deployed cells experiencing negligible interference and assume perfect channel state information (CSI). For this setting, we propose an algorithm that finds the SE and EE resource allocation strategies. Then, we compare the performance of both design strategies versus number of users, and phantom cells share of the total available resource units (RUs). We aim to investigate the effect of some system parameters to achieve improved SE performance at a non-significant loss in EE performance, or vice versa. It is found that increasing phantom cells share of RUs decreases the SE performance loss due to EE optimization when compared with the optimized SE performance. Second, we consider the densely deployed phantom cellular networks and model the EE optimization problem having into consideration the inevitable interference and imperfect channel estimation. To this end, we propose three resource allocation strategies aiming at optimizing the EE performance metric of this network. Furthermore, we investigate the effect of changing some of the system parameters on the performance of the proposed strategies, such as phantom cells share of RUs, number of deployed phantom cells within a macro cell coverage, number of pilots and the maximum power available for transmission by the phantom cells BSs. It is found that increasing the number of pilots deteriorates the EE performance of the whole setup, while increasing maximum power available for phantom cells transmissions reduces the EE of the whole setup in a less severe way than increasing the number of pilots. It is found also that increasing phantom cells share increases the EE metric in the dense deployment case. Thus, it is always useful to allocate most of the network RUs to the phantom cells tier.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22589号 / 情博第726号 / 新制||情||124(附属図書館) / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 守倉 正博, 教授 原田 博司, 教授 大木 英司 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Dengue mitigation is a major research area among scientist who are working towards an effective management of the dengue epidemic. An effective dengue mitigation requires several other important components. These components include an accurate epidemic modeling, an efficient epidemic prediction, and an efficient resource allocation for controlling of the spread of the dengue disease. Past studies assumed homogeneous response pattern of the dengue epidemic to climate conditions throughout the regions. The dengue epidemic is climate dependent and also it is geographically dependent. A global model is not sufficient to capture the local variations of the epidemic. We propose a novel method of epidemic modeling considering local variation and that uses micro ensemble of regressors for each region. There are three regressors that are used in the construction of the ensemble. These are support vector regression, ordinary least square regression, and a k-nearest neighbor regression. The best performing regressors get selected into the ensemble. The proposed ensemble determines the risk of dengue epidemic in each region in advance. The risk is then used in risk-based resource allocation. The proposing resource allocation is built based on the genetic algorithm. The algorithm exploits the genetic algorithm with major modifications to its main components, mutation and crossover. The proposed resource allocation converges faster than the standard genetic algorithm and also produces a better allocation compared to the standard algorithm.

Content distribution with mobile-to-mobile cooperation is studied. Data is sent to mobile terminals on a long range link then the terminals exchange the content using an appropriate short range wireless technology. Unicasting and multicasting are investigated, both on the long range and short range links. Energy minimization is formulated as an optimization problem for each scenario, and the optimal solutions are determined in closed form. Moreover, the schemes are applied in public safety vehicular networks, where Long Term Evolution (LTE) network is used for the long range link, while IEEE 802.11 p is considered for inter-vehicle collaboration on the short range links. Finally, relay-based multicasting is applied in high speed trains for energy and delay minimization. Results show that cooperative schemes outperform non-cooperative ones and other previous related work in terms of energy and delay savings. Furthermore, practical implementation aspects of the proposed methods are also discussed.

Augmented input refers to the support of any form of linguistic or visual strategies to enhance understanding during intervention. Previous research predominantly focused on the various types of augmented input that can be used, especially to support reading comprehension. The purpose of this study was to determine and compare the effect of varying amounts of augmented input using partner-pointing on the accuracy of auditory comprehension for persons with Wernicke’s aphasia specifically. The research was conducted with seven participants with Wernicke’s aphasia. The participants listened to three narratives in three conditions, namely 0%, 50% and 100% augmented input with partner-pointing, and then responded to comprehension items based on the narratives. Most participants had more accurate scores during the 50% augmented input condition. In addition, participants did significantly better in the 50% condition than in the 100% augmented input condition. The main clinical implication is that supporting narrative auditory comprehension with augmented input, used as pre-task and during-task stimulation, seems to facilitate the improved auditory comprehension of narratives for some persons with Wernicke’s aphasia. However, providing augmented input for all the content units of a narrative seems to have a negative effect on the auditory comprehension of some persons with Wernicke’s aphasia. Continued research is necessary to determine what types and frequency of augmented input will lead to improved auditory comprehension for persons with aphasia, specifically Wernicke’s aphasia. / Dissertation (MA)--University of Pretoria, 2019. / Centre for Augmentative and Alternative Communication (CAAC) / MA / Unrestricted

With the emergence of the Internet-of-Things (IoT), communication networks have evolved toward autonomous networks of intelligent devices capable of communicating without direct human intervention. This is known as Machine-to-Machine (M2M) communications. Cellular networks are considered one of the main technologies to support the deployment of M2M communications as they provide extended wireless connectivity and reliable communication links. However, the characteristics and Quality-of-Service (QoS) requirements of M2M communications are distinct from those of conventional cellular communications, also known as Human-to-Human (H2H) communications, that cellular networks were originally designed for. Thus, enabling M2M communications poses many challenges in terms of interference, congestion, spectrum scarcity and energy efficiency. The primary focus is on the problem of resource allocation that has been the interest of extensive research effort due to the fact that both M2M and H2H communications coexist in the cellular network. This requires that radio resources be allocated such that the QoS requirements of both groups are satisfied. In this work, we propose three models to address this problem. In the first model, a two-phase resource allocation algorithm for H2H/M2M coexistence in cellular networks is proposed. The goal is to meet the QoS requirements of H2H traffic and delay-sensitive M2M traffic while ensuring fairness for the delay-tolerant M2M traffic. Simulation results are presented which show that the proposed algorithm is able to balance the demands of M2M and H2H traffic, meet their diverse QoS requirements, and ensure fairness for delay-tolerant M2M traffic. With the growing number of Machine-Type Communication Devices (MTCDs) the problem of spectrum scarcity arises. Hence, Cognitive Radio (CR) is the focus of the second model where clustered Cognitive M2M (CM2M) communications underlaying cellular networks is proposed. In this model, MTCDs are grouped in clusters based on their spatial location and communicate with the Base Station (BS) via Machine-Type Communication Gateways (MTCGs). An underlay CR scheme is implemented where the MTCDs within a cluster share the spectrum of the neighbouring Cellular User Equipment (CUE). A joint resource-power allocation problem is formulated to maximize the sum-rate of the CUE and clustered MTCDs while adhering to MTCD minimum data rate requirements, MTCD transmit power limits, and CUE interference constraints. Simulation results are presented which show that the proposed scheme significantly improves the sum-rate of the network compared to other schemes while satisfying the constraints. Due to the limited battery capacity of MTCDs and diverse QoS requirements of both MTCDs and CUE, Energy Efficiency (EE) is critical to prolonging network lifetime to ensure uninterrupted and reliable data transmission. The third model investigates the power allocation problem for energy-efficient CM2M communications underlaying cellular networks. Underlay CR is employed to manage the coexistence of MTCDs and CUE and exploit spatial spectrum opportunities. Two power allocation problems are proposed where the first targets MTCD power consumption minimization while the second considers MTCD EE maximization subject to MTCD transmit power constraints, MTCD minimum data rate requirements, and CUE interference limits. Simulation results are presented which indicate that the proposed algorithms provide MTCD power allocation with lower power consumption and higher EE than the (Equal Power Allocation) EPA scheme while satisfying the constraints. / Graduate

This thesis presents a research effort aimed at developing using construction methods and techniques in army tactical shelter. The beginning step focuses on developing and identifying different activities and work breakdown structure applicable in shelter prototype. The next step focuses on identifying resource allocation. This include allocate resources based on the delivered project as per alternative one and for the second alternative as optimization, resource allocation modified and tried to level and minimize resource peak. In addition, the cost calculated for the whole project as well as for each WBS and activities which consider as alternative one and in the second alternative, cost mitigation applied according to available resources and adjusting predecessors and successors of each activity. In conclusion, two alternatives compared, available outcome presents, and future work suggested for the project team to continue this effort.