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Lu, Lu. "Spectral-efficient design in modern wireless communications networks." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53902.
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We investigate spectral-efficient design and develop novel schemes to improve spectral efficiency of the modern wireless communications networks. Nowadays, more and more spectrum resources are required to support various high-data-rate applications while spectrum resources are limited. Moreover, static allocation and exclusive access in current spectrum assignment policy caused a lot of licensed spectrum bands to be underutilized. To deal with the problem, cognitive radio (CR) has been developed, which allows unlicensed/secondary users to transmit with licensed/primary users as long as the former ones do not generate intolerable interference to the latter ones. The coexistence of users and networks requires careful and dynamic planning to mitigate interference. Otherwise, the network performance will be severely undermined. We study both spectrum sensing and spectrum access techniques and propose several transmit schemes for different types of cognitive ratio networks, including spectrum overlay and spectrum underlay systems. The proposed algorithms can improve spectral efficiency of the networks efficiently and have potentials to be used in future wireless communications networks.
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Bandeira, Nuno Filipe Cabrita. "Spectral networks algorithms for de novo interpretation of tandem mass spectra." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3274510.
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Thesis (Ph. D.)--University of California, San Diego, 2007.Title from first page of PDF file (viewed October 2, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 141-152).
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Smith, Dallas C. "Network Specializations, Symmetries, and Spectral Properties." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/6998.
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In this dissertation, we introduce three techniques for network sciences. The first of these techniques is a series of new models for describing network growth. These models, called network specialization models, are built with the idea that networks grow by specializing the function of subnetworks. Using these models we create theoretical networks which exhibit well-known properties of real networks. We also demonstrate how the spectral properties are preserved as the models grow. The second technique we describe is a method for decomposing networks that contain automorphisms in a way that preserves the spectrum of the original graph. This method for graph (or equivalently matrix) decomposition is called an equitable decomposition. Previously this method could only be used for particular classes of automorphisms, but in this dissertation we have extended this theory to work for every automorphism. Further we explain a number of applications which use equitable decompositions. The third technique we describe is a generalization of network symmetry, called latent symmetry. We give numerous examples of networks which contain latent symmetries and also prove some properties about them
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Shorten, David. "Spectral analysis of neutral evolution." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27420.
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It has been argued that much of evolution takes place in the absence of fitness gradients. Such periods of evolution can be analysed by examining the mutational network formed by sequences of equal fitness, that is, the neutral network. It has been demonstrated that, in large populations under a high mutation rate, the population distribution over the neutral network and average mutational robustness are given by the principal eigenvector and eigen- value, respectively, of the network's adjacency matrix. However, little progress has been made towards understanding the manner in which the topology of the neutral network influences the resulting population distribution and robustness. In this work, we build on recent results from spectral graph theory and utilize numerical methods to enhance our understanding of how populations distribute themselves over neutral networks. We demonstrate that, in the presence of certain topological features, the population will undergo an exploration catastrophe and become confined to a small portion of the network. We further derive approximations, in terms of mutational biases, for the population distribution and average robustness in networks with a homogeneous structure. The applicability of these results is explored, first, by a detailed review of the literature in both evolutionary computing and biology concerning the structure of neutral networks. This is extended by studying the actual and predicted population distribution over the neutral networks of H1N1 and H3N2 influenza haemagglutinin during seasons between 2005 and 2016. It is shown that, in some instances, these populations experience an exploration catastrophe. These results provide insight into the behaviour of populations on neutral networks, demonstrating that neutrality does not necessarily lead to an exploration of genotype/phenotype space or an associated increase in population diversity. Moreover, they provide a plausible explanation for conflicting results concerning the relationship between robustness and evolvability.
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Miao, Guowang. "Cross-layer optimization for spectral and energy efficiency." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31807.
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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.Committee Chair: Li, Geoffrey Ye; Committee Member: Ma, Xiaoli; Committee Member: Stuber, Gordon; Committee Member: Wardi, Yorai; Committee Member: Yu, Xingxing. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Yamamoto, Koji. "Capacity and Spectral Efficiency of Multihop Radio Networks." 京都大学 (Kyoto University), 2005. http://hdl.handle.net/2433/68891.
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Adouane, Amine Mohamed. "Dynamic management of spectral resources in LTE networks." Thesis, Versailles-St Quentin en Yvelines, 2015. http://www.theses.fr/2015VERS007V/document.
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La croissance exponentielle du nombre de dispositifs communicants et des services sans fils émergents fixe des objectifs toujours plus haut pour répondre à la demande de capacité sans cesse croissante des utilisateurs. Cela pose des défis constants pour atteindre les objectifs envisagés. La réutilisation spectrale élevée (High efficiency spectral reuse) a été adopté, cependant, elle conduit à des interférences accrues sur le réseau, ce qui dégrade les performances. L'OFDM (Orthogonal Frequency Division Multiplexing) est utilisé comme solution dans les réseaux de 4 G. Grâce à son orthogonalité, l'OFDM élimine l'interférence intra-cellulaire, mais l'interférence inter-cellule reste importante. Plusieurs méthodes connues sous le nom d'Inter-Cell interférences coordination (ICIC) ont été proposées pour les diminuer. L'ICIC permet la gestion des ressources radio coordonnée entre plusieurs cellules appelées ENodeB. Ces eNodeB peuvent partager les informations nécessaires grâce à l'interface X2 qui les relient, ces informations sont transmises par des messages LTE normalisés. Lorsque les ENodeBs sélectionnent égoïstement les ressources, la théorie de jeux non-coopératifs est largement appliquée pour trouver un juste équilibre. Dans cette thèse, nous mettons l'accent sur l'ICIC pour la liaison descendante d'un système OFDMA cellulaire dans le contexte du projet SOAPS (Spectrum opportuniste accès à la Sécurité publique). Ce projet a pour but l'amélioration de la planification des ressources de fréquences pour fournir des services à large bande dans les systèmes PMR (radiocommunications mobiles privées) en utilisant les technologies LTE. Nous adressons le problème d'ICIC en proposant quatre solutions différentes sous forme d'algorithmes entièrement décentralisés, ces algorithmes se basent sur la théorie des jeux non-coopératifs avec des équilibres de Nash purs des jeux considérésThe exponential growth in the number of communications devices has set out new ambitious targets to meet the ever-increasing demand for user capacity in emerging wireless systems. However, the inherent impairments of communication channels in cellular systems pose constant challenges to meet the envisioned targets. High spectral reuse efficiency was adopted as a solution to higher data rates. Despite its benefits, high spectral reuse leads to increased interference over the network, which degrades performances of mobile users with bad channel quality. To face this added interfence, OFDM (Orthogonal Frequency Division Multiplexing) is used for the new 4th generation network. Thanks to its orthogonality OFDM eliminates the intra-cellular interference, but when the same resources are used in two adjacents cells, the inter-cell interference becomes severe. To get rid of the latter, several methods for Inter-Cell Interference Coordination (ICIC) have been proposed. ICIC allows coordinated radio resources management between multiple cells. The eNodeBs can share resource usage information and interference levels over the X2 interface through LTE-normalized messages. Non-cooperative game theory was largely applied were eNodeBs selfishly selects resource blocks (RBs) in order to minimize interference. In this thesis, we stress on ICIC for the downlink of a cellular OFDMA system in the context of the SOAPS (Spectrum Opportunistic Access in Public Safety) project. This project focuses on the improvement of frequency resource scheduling for Broadband Services provision by PMR (Private Mobile Radio) systems using LTE technologies. We addressed this problem with four different solutions based on Non-cooperative game theory, three algorithms are devoted to RB selection in order to manage the interference, while the last one is a power control scheme with power economy and enhanced system performances
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Ahmed, Junaid. "Spectral efficiency of CDMA based ad-hoc networks." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/spectral-efficiency-of-cdma-based-adhoc-networks(f6d958ac-6778-416e-80a5-2318956dbaf2).html.
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Spectrum efficiency and energy efficiency are two important attributes driving innovation in wireless communication. Efficient spectrum utilization and sharing with multiple access techniques and using under-utilized spectra by cognitive radios is the current focus due to the scarcity and cost of the available radio spectrum. Energy efficiency to increase operating time of portable handheld devices like smartphones that handle simultaneous voice/video streaming and web browsing and battery powered nodes in a sensor network where battery capacity determines the lifetime of the network is another area attracting researchers. The focus of this thesis is the spectral efficiency of multicarrier code division multiple access (CDMA) in wireless ad-hoc networks. Furthermore, energy efficiency to maximize lifetime of a network are also studied.In a multicarrier CDMA system inter-carrier interference (ICI) due to carrier frequency offset and multiple access interference (MAI) are two major factors that deteriorate the performance. Previous work in this area has been mostly focused on simulation results due to the complexity of the analysis due to the large number of random variables involved. Taking into account accurate statistical models for ICI and MAI that account for the correlation between adjacent subcarriers, this thesis presents new mathematical analysis for the spectral efficiency of multicarrier CDMA communication systems over a frequency selective Rayleigh fading environment. We analyze and compare three multicarrier CDMA schemes which are multicarrier CDMA, multicarrier direct-sequence CDMA and multitone CDMA. We also present simulation results to confirm the validity of our analysis. We also analyze the performance of three simple multiple access techniques or coexistence etiquettes in detail, which are simple to implement and do not require any central control. Accurate interference models are developed and are used to derive accurate expressions for packet error rates in the case of direct sequence CDMA and slotted packet transmission schemes. These results are then used to study the performance of the coexistence etiquettes and compare them with each other. Finally we present a new joint node selection and power allocation strategy that increases lifetime of an ad-hoc network where nodes cooperate to enable diversity in transmission.
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Lahsen-Cherif, Iyad. "Spectral and Energy Efficiency in 5G Wireless Networks." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLS506/document.
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La pénurie d'énergie et le manque d'infrastructures dans les régions rurales représentent une barrière pour le déploiement et l'extension des réseaux cellulaires. Les approches et techniques pour relier les stations de base (BSs) entre elles à faible coût et d'une manière fiable et efficace énergiquement sont l'une des priorités des opérateurs. Ces réseaux peu denses actuellement, peuvent évoluer rapidement et affronter une croissance exponentielle due principalement à l'utilisation des téléphones mobiles, tablettes et applications gourmandes en bande passante. La densification des réseaux est l'une des solutions efficaces pour répondre à ce besoin en débit élevé. Certes, l'introduction de petites BSs apporte de nombreux avantages tels que l'amélioration du débit et de la qualité du signal, mais entraîne des contraintes opérationnelles telles que le choix de l'emplacement des noeuds dans ces réseaux de plus en plus denses ainsi que leur alimentation. Les problèmes où la contrainte spatiale est prépondérante sont bien appropriés à la modélisation par la géométrie stochastique qui permet une modélisation réaliste de distribution des BSs. Ainsi, l'enjeu est de trouver de nouvelles approches de gestions d'interférence et de réductions de consommation énergétique dans les réseaux sans fil. Le premier axe de cette thèse s'intéresse aux méthodes de gestion d'interférence dans les réseaux cellulaires se basant sur la coordination entre les BSs, plus précisément, la technique Coordinated MultiPoint Joint Transmission (CoMP-JT). En CoMP-JT, les utilisateurs en bordure de cellules qui subissent un niveau très élevé d'interférences reçoivent plusieurs copies du signal utile de la part des BSs qui forment l'ensemble de coordination. Ainsi, nous utilisons le modèle r-l Square Point Process (PP) à fin de modéliser la distribution des BSs dans le plan. Le processus r-l Square PP est le plus adapté pour modéliser le déploiement réel des BSs d'un réseau sans fil, en assurant une distance minimale, (r - l), entre les points du processus. Nous discutons l'impact de la taille de l'ensemble de coordination sur les performances évaluées. Ce travail est étendu pour les réseaux denses WiFi IEEE 802.11, où les contraintes de portées de transmission et de détection de porteuse ont été prises en compte. Dans le deuxième axe du travail, nous nous intéressons à l'efficacité énergétique des réseaux mesh. Nous proposons l'utilisation des antennes directionnelles (DAs) pour réduire la consommation énergétique et améliorer le débit de ces réseaux mesh. Les DAs ont la capacité de focaliser la transmission dans la direction du récepteur, assurant une portée plus importante et moins d'énergie dissipée dans toutes les directions. Pour différentes topologies, nous dérivons le nombre de liens et montrons que ce nombre dépend du nombre de secteurs de l'antenne. Ainsi, en utilisant les simulations, nous montrons que le gain, en énergie et en débit, apporté par les DAs peut atteindre 70% dans certains cas. De plus, on propose un modèle d'optimisation conjointe d'énergie et du débit adapté aux réseaux WMNs équipés de DAs. La résolution numérique de ce modèle conforte les résultats de simulation obtenus dans la première partie de cette étude sur l'impact des DAs sur les performances du réseau en termes de débit et d'énergie consommée. Ces travaux de thèse s'inscrivent dans le cadre du projet collaboratif (FUI16 LCI4D), qui consiste à concevoir et à valider une architecture radio ouverte pour renforcer l'accès aux services broadband dans des lieux ne disposant que d'une couverture minimale assurée par un réseau macro-cellulaire traditionnelToday's networks continue to evolve and grow resulting more dense, complex and heterogeneous networks.This leads to new challenges such as finding new models to characterize the nodes distribution in the wireless network and approaches to mitigate interference. On the other hand, the energy consumption of WMNs is a challenging issue mainly in rural areas lacking of default electrical grids. Finding alternative technologies and approaches to reduce the consumed energy of these networks is a interesting task. This thesis focuses on proposing and evaluating interference management models for next generation wireless networks (5G and Very Dense High WLANs), and providing tools and technologies to reduce energy consumption of Wireless Mesh Networks (WMNs). Two different problems are thus studied; naturally the thesis is divided into two parts along the following chapters.The contribution of the first part of the thesis is threefold. Firstly, we develop our interference management coordination (CoMP-JT) model. The main idea of CoMP-JT is to turn signals generating harmful interference into useful signals. We develop a new model where BSs inside the coordinated set send a copy of data to border's users experiencing high interference. We consider the r-l Square point process to model the BSs distribution in the network. We derive network performance in terms of coverage probability and throughput. Additionally, we study the impact of the size of coordination set on the network performance. Secondly, we extend these results and provide a new model adopted for Dense Very high throughput WLANs. We take into consideration constraints of WLANs in our model such as carrier sensing range. Thirdly, we tackle resource allocation strategies to limit the interference in LTE networks. We study three cyclic allocation strategies: (i) the independent allocation, (ii) the static allocation and (iii) the load-dependent strategy. We derive tractable analytical expression of the first and second mean of interference. We validate the model using extensive simulations. Reducing the energy consumption and improving the energy efficiency of WMNs is our concern in the second part of the thesis. Indeed, we aim at studying the impact of directional antennas technology on the performance of WMNs, using both analysis and simulations. Fisrt, We derive the Number of Links (NLs) for the chain and grid topologies for different antennas beams. These results are based on the routing tables of nodes in the network. We consider different scenarios such as 1Source-NDestinations to model the downlink communications, NSources-1Destination to model the uplink communications and the 1Source-1Destination as a baseline scenario. Using ns-3 simulator, we simulate network performance in terms of Mean Loss Ratio, throughput, energy consumption and energy efficiency. Then, we study the impact of number of beams, network topology and size, the placement of the gateway on the network performance. Next, we go beyond simulations and propose an optimization framework minimizing the consumed energy while maximizing the network throughput for DAs WMNs. We consider a weighted objective function combining the energy consumption and the throughput. We use power control to adapt transmission power depending on the location of the next hop. This model is a first step to approve the obtained simulation results. We use ILOG Cplex solver to find the optimal solution. Results show that DAs improves the network throughput while reduce the energy consumption and that power control allows saving more energy. In this direction, the LCI4D Project aims at providing low cost infrastructure to connect isolated rural and sub-urban areas to the Internet. In order to reduce the installation and maintenance costs, LCI4D proposes the usage of self-configured Wireless Mesh Networks (WMNs) to connect multimode outdoor femtocells to the remote Marco cell (gateway)
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Kunegis, Jérôme [Verfasser]. "On the Spectral Evolution of Large Networks / Jérôme Kunegis." Koblenz : Universitätsbibliothek Koblenz, 2011. http://d-nb.info/1017370893/34.
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Rooker, Terry. "Formant estimation from a spectral slice using neural networks /." Full text open access at:, 1990. http://content.ohsu.edu/u?/etd,257.
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Akbar, Sunila. "Heterogeneous cellular networks with energy and spectral efficient techniques." Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/heterogeneous-cellular-networks-with-energy-and-spectral-efficient-techniques(032b2fb8-07ab-474f-b9b7-1b93a4af29f0).html.
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Owing to the dramatic increase in the smart devices' users in quest of high link capacity, the design of the next generation of wireless networks will necessarily have to consider spectral and energy effciency as the key pillars. The future wireless heterogeneous cellular networks (HCNs), featuring planned base stations (BSs), overlaid with unplanned micro, pico and femto BSs, can provide substantial gains in throughput and user experience as compared to the conventional homogeneous networks. My research is focusing on developing analytical models for HCNs employing spectrum and energy efficient techinques using tools from stochastic geometry. The first work is motivated to jointly support energy sustainability and high throughput performance by integrating simultaneous information and wireless power transfer (SWIPT) with HCNs. In this work, a tractable model for joint uplink (UL) and downlink (DL) transmission in a K-tier HCN with SWIPT is developed where the mobile users (MUs) decode information as well as harvest energy in the DL. The harvested energy is then utilized for UL information transmission. The analytical expressions for the DL average received power, the DL and UL outage probabilities and average ergodic rates are derived for the system design. The UL performance of a MU is shown to be improved by increasing the fraction of the DL received power for energy harvesting in the network, whereas the energy eciency is shown to be improved with the increase in SBSs density. The second work proposed a K-tier HCNs wherein the macrocell tier comprises half duplex (HD) BSs and the small cell tiers consist of full duplex (FD) BSs. In theory, FD data transmission is capable of doubling the spectral eciency with the same amount of energy compared to that of half-duplex (HD) system. The FD communication is considered at the small cell BSs only due to their low-powered nature and ease of deployment. The performance of the proposed HCNs is evaluated in terms of the DL and UL average ergodic rates which is shown to be improved as compared to the conventional HCNs where all tiers operate in HD mode. An important challenge in HCNs with FD small cells is the decrease in coverage due to the increased interference from simultaneous DL and UL operations on the same band in FD mode. This motivates to consider massive multiuser multiple-input multiple-output (MIMO) at the macrocells, which is a promising wireless communication technology for improved coverage and cell edge performance. In the third work, HCNs with massive MIMO antennas at the macrocell BSs and FD small cell is studied. Since, UL power control further improves the coverage performance of the cell edge MUs and eciently utilize their battery, distance proportional fractional power control has been considered as well. It is shown that the link reliability and area spectral eciency of the network can be signi cantly leveraged by taking advantage of FD small cell BSs density and the number of antennas at the macrocell BSs. At the end, according to the overall picture of the research conducted, the main conclusions together with some directions for the future work are presented.
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Hu, JIngming. "Spectral Sampling for Visual Analytics of Big Complex Networks." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21263.
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Visual analysis is one of the most effective methods of analyzing large complex networks, and diverse research directions for analyzing and sampling large complex networks are being pursued. One approach is to replace the original graph with a much smaller one while maintaining high quality; this is called the proxy graph approach. However, research has demonstrated that it is a challenge to compute a high-quality proxy graph to represent the original graph. It is also expensive to label the structural properties of the network, especially in terms of time consumption. This thesis introduces new methods for computing proxy graphs based on spectral sparsification approaches for visualizing large complex networks. Two types of spectral sparsification approaches are proposed: 1. We introduce a new method called spectral sampling vertex (SV) for computing proxy graphs. This method reduces the number of vertices in a graph while retaining its structural properties, based on the high effective resistance value. Extensive experimental results using graph sampling quality metrics, visual comparison, and proxy quality metrics confirm that our new method significantly outperforms the Random Vertex sampling method and the Degree Centrality-based sampling method. 2. We introduced two divide and conquer methods for spectral sparsification: BC Tree-based Spectral Sparisification (BC_SS) and BC Tree-based Spectral Vertex Sampling (BC_SV). These two methods are based on the decomposition of a connected graph into biconnected components. Experimental results show that our methods are significantly faster than the pre- vious method while preserving similar sparsification results.
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Arbi, Abdelrahman. "Spectral and energy efficiency in cellular mobile radio access networks." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/18518/.
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Driven by the widespread use of smartphones and the release of a wide range of online packet data services, an unprecedented growth in the mobile data usage has been observed over the last decade. Network operators recently realised that the traditional approach of deploying more macrocells could not cope with this continuous growth in mobile data traffic and if no actions are taken, the energy demand to run the networks, which are able to support such traffic volumes risks to become unmanageable. In this context, comprehensive investigations of different cellular network deployments, and various algorithms have been evaluated and compared against each other in this thesis, to determine the best deployment options which are able to deliver the required capacity at a minimum level of energy consumption. A new scalable base station power consumption model was proposed and a joint evaluation framework for the relative improvements in throughput, energy consumption,and energy efficiency is adopted to avoid the inherent ambiguity of using only the bit/J energy efficiency metric. This framework was applied to many cellular network cases studies including macro only, small cell only and heterogeneous networks to show that pure small cell deployments outperform the macro and heterogeneous networks in terms of the energy consumption even if the backhaul power consumption is included in the analysis. Interestingly, picocell only deployments can attain up to 3 times increase in the throughput and 2.27 times reduction in the energy consumed when compared with macro only RANs at high target capacities, while it offers 2 times more throughput and reduces the energy consumption by 12% when compared with the macro/pico HetNet deployments. Further investigations have focused on improving the macrocell RAN by adding more sectors and more antennas. Importantly, the results have shown that adding small cells to the macrocell RAN is more energy efficient than adding more sectors even if adaptive sectorisation techniques are employed. While dimensioning the network by using MIMO base stations results in less consumed energy than using SISO base stations. The impact of traffic offloading to small cell, sleep mode, and inter-cell interference coordination techniques on the throughput and energy consumption in dense heterogeneous network deployments have been investigated. Significant improvements in the throughput and energy efficiency in bit/J were observed. However, a decrease in the energy consumption is obtained only in heterogeneous networks with small cells deployed to service clusters of users. Finally, the same framework is used to evaluate the throughput and energy consumption of massive MIMO deployments to show the superiority of massive MIMOs versus macrocell RANs, small cell deployments and heterogeneous networks in terms of achieving the target capacity with a minimum level of energy consumption. 1.6 times reduction in the energy consumption is achieved by massive MIMOs when compared with picocell only RAN at the same target capacity and when the backhaul power consumption is included in the analysis.
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Ku, Ivan Chui Choon. "Spectral-energy efficiency trade-off of relay-aided cellular networks." Thesis, Heriot-Watt University, 2013. http://hdl.handle.net/10399/2698.
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Wireless communication networks are traditionally designed to operate at high spectral e ciency with less emphasis on power consumption as it is assumed that endless power supply is available through the power grid where the cells are connected to. As new generations of mobile networks exhibit decreasing gains in spectral e ciency, the mobile industry is forced to consider energy reform policies in order to sustain the economic growth of itself and other industries relying on it. Consequently, the energy e ciency of conventional direct transmission cellular networks is being examined while alternative green network architectures are also explored. The relay-aided cellular network is being considered as one of the potential network architecture for energy e cient transmission. However, relaying transmission incurs multiplexing loss due to its multi-hop protocol. This, in turn, reduces network spectral e ciency. Furthermore, interference is also expected to increase with the deployment of Relay Stations (RSs) in the network. This thesis examines the power consumption of the conventional direct transmission cellular network and contributes to the development of the relay-aided cellular network. Firstly, the power consumption of the direct transmission cellular network is investigated. While most work considered transmitter side strategies, the impact of the receiver on the Base Station (BS) total power consumption is investigated here. Both the zero-forcing and minimum mean square error weight optimisation approaches are considered for both the conventional linear and successive interference cancellation receivers. The power consumption model which includes both the radio frequency transmit power and circuit power is described. The in uence of the receiver interference cancellation techniques, the number of transceiver antennas, circuit power consumption and inter-cell interference on the BS total power consumption is investigated. Secondly, the spectral-energy e ciency trade-o in the relay-aided cellular network is investigated. The signal forwarding and interference forwarding relaying paradigms are considered with the direct transmission cellular network taken as the baseline. This investigation serves to understand the dynamics in the performance trade-o . To select a suitable balance point in the trade-o , the economic e ciency metric is proposed whereby the spectral-energy e ciency pair which maximises the economic pro tability is found. Thus, the economic e ciency metric can be utilised as an alternative means to optimise the relay-aided cellular network while taking into account the inherent spectral-energy e ciency trade-o . Finally, the method of mitigating interference in the relay-aided cellular network is demonstrated by means of the proposed relay cooperation scheme. In the proposed scheme, both joint RS decoding and independent RS decoding approaches are considered during the broadcast phase while joint relay transmission is employed in the relay phase. Two user selection schemes requiring global Channel State Information (CSI) are considered. The partial semi-orthogonal user selection method with reduced CSI requirement is then proposed. As the cooperative cost limits the practicality of cooperative schemes, the cost incurred at the cooperative links between the RSs is investigated for varying degrees of RS cooperation. The performance of the relay cooperation scheme with di erent relay frequency reuse patterns is considered as well. In a nutshell, the research presented in this thesis reveals the impact of the receiver on the BS total power consumption in direct transmission cellular networks. The relayaided cellular network is then presented as an alternative architecture for energy e cient transmission. The economic e ciency metric is proposed to maximise the economic pro tability of the relay network while taking into account the existing spectral-energy e ciency trade-o . To mitigate the interference from the RSs, the relay cooperation scheme for advanced relay-aided cellular networks is proposed.
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Alam, Ahmad Mahbubul. "Energy efficiency-spectral efficiency tradeoff in interference-limited wireless networks." Thesis, Rennes, INSA, 2017. http://www.theses.fr/2017ISAR0028/document.
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L'une des stratégies utilisée pour augmenter l'efficacité spectrale (ES) des réseaux cellulaires est de réutiliser la bande de fréquences sur des zones relativement petites. Le problème majeur dans ce cas est un plus grand niveau d'interférence, diminuant l'efficacité énergétique (EE). En plus d'une plus grande largeur de bande, la densification des réseaux (cellules de petite taille ou multi-utilisateur à entrées multiples et sortie unique, MU-EMSO), peut augmenter l'efficacité spectrale par unité de surface (ESuS). La consommation totale d'énergie des réseaux sans fil augmente en raison de la grande quantité de puissance de circuit consommée par les structures de réseau denses, réduisant l'EE. Dans cette thèse, la région EE-SE est caractérisé dans un réseau cellulaire hexagonal en considérant plusieurs facteurs de réutilisation de fréquences (FRF), ainsi que l'effet de masquage. La région EE-ESuS est étudiée avec des processus de Poisson ponctuels (PPP) pour modéliser un réseau MU-EMSO avec un précodeur à rapport signal sur fuite plus bruit (RSFB). Différentes densités de station de base (SB) et nombre d'antennes aux SB avec une consommation d'énergie statique sont considérées.Nous caractérisons d'abord la région EE-SE dans le réseau cellulaire hexagonal pour différentes FRF, avec et sans masquage. Avec le masquage en plus de la perte de propagation, la mesure de coupure ε-EE-ES est proposée pour évaluer les performances. Les courbes EE-ES présentent une grande partie linéaire, due à la consommation de puissance statique, suivie d'une forte diminution de l'EE, puisque le réseau est homogène et limité par les interférences. Les résultats montrent qu'un FRF de 1 pour les régions proches de la SB et des FRF plus élevés dans la région plus proche du bord de la cellule améliorent le point optimal du EE-ES. De plus, un meilleur compromis EE-ES peut être obtenu avec une valeur plus élevée de coupure. En outre, un FRF de 1 est le meilleur choix pour une valeur élevée de coupure en raison d'une réduction du rapport signal sur interférence plus bruit (RSIB).Les précodeurs sont utilisés en liaison descendante des réseaux cellulaires MU-EMSO à accès multiple par division spatiale (AMDS) pour améliorer le RSIB. La géométrie stochastique a été utilisée intensivement pour analyser de tels systèmes complexes. Nous obtenons une expression analytique de l'ESuS en régime asymptotique, c.-à-d. nombre d'antennes et d'utilisateurs infinis, en utilisant des résultats de matrices aléatoires et de géométrie stochastique. Les SBs et les utilisateurs sont modélisés par deux PPP indépendants et le précodage RSFB est utilisé. L'EE est dérivée d'un modèle de consommation de puissance linéaire. Les simulations de Monte Carlo montrent que les expressions analytiques sont précises même pour un nombre faible d'antennes et d'utilisateurs. De plus, les courbes d'EE-ESuS ont une grande partie linéaire avant une forte décroissante de l'EE, comme pour les réseaux hexagonaux. Les résultats montrent également que le précodeur RSFB offre de meilleurs performances que le précodeur forçage à zéro (FZ), qui est typiquement utilisé dans la literature. Les résultats numériques pour le précodeur RSFB montrent que déployer plus de SBs ou d'antennes aux BSs augmente l'ESuS, mais que le gain dépend du rapport des densités SB-utilisateurs et du nombre d'antennes lorsque la densité de l'utilisateur est fixe. L'EE augmente seulement lorsque l'augmentation de l'ESuS est plus importante que l'augmentation de la consommation d'énergie par unité de surface. D'autre part, lorsque la densité d'utilisateur augmente, l'ESuS dans la région limitée par les interférences peut être améliorée en déployant davantage de SB sans sacrifier l'EE et le débit ergodique des utilisateursOne of the used strategies to increase the spectral efficiency (SE) of cellular network is to reuse the frequency bandwidth over relatively small areas. The major issue in this case is higher interference, decreasing the energy efficiency (EE). In addition to the higher bandwidth, densification of the networks (e.g. small cells or multi-user multiple input single output, MU-MISO) potentially increases the area spectral efficiency (ASE). The total energy consumption of the wireless networks increases due to the large amount of circuit power consumed by the dense network structures, leading to the decrease of EE. In this thesis, the EE-SE achievable region is characterized in a hexagonal cellular network considering several frequency reuse factors (FRF), as well as shadowing. The EE-ASE region is also studied using Poisson point processes (PPP) to model the MU-MISO network with signal-to-leakage-and-noise ratio (SLNR) precoder. Different base station (BS) densities and different number of BS antennas with static power consumption are considered.The EE-SE region in a hexagonal cellular network for different FRF, both with and without shadowing is first characterized. When shadowing is considered in addition to the path loss, the ε-SE-EE tradeoff is proposed as an outage measure for performance evaluation. The EE-SE curves have a large linear part, due to the static power consumption, followed by a sharp decreasing EE, since the network is homogeneous and interference-limited. The results show that FRF of 1 for regions close to BS and higher FRF for regions closer to the cell edge improve the EE-SE optimal point. Moreover, better EE-SE tradeoff can be achieved with higher outage values. Besides, FRF of 1 is the best choice for very high outage value due to the significant signal-to-interference-plus-noise ratio (SINR) decrease.In downlink, precoders are used in space division multiple access (SDMA) MU-MISO cellular networks to improve the SINR. Stochastic geometry has been intensively used to analyse such a complex system. A closed-form expression for ASE in asymptotic regime, i.e. number of antennas and number of users grow to infinity, has been derived using random matrix theory and stochastic geometry. BSs and users are modeled by two independent PPP and SLNR precoder is used at BS. EE is then derived from a linear power consumption model. Monte Carlo simulations show that the analytical expressions are tight even for moderate number of antennas and users. Moreover, the EE-ASE curves have a large linear part before a sharply decreasing EE, as observed for hexagonal network. The results also show that SLNR outperforms the zero-foring (ZF) precoder, which is typically used in literature. Numerical results for SLNR show that deploying more BS or a large number of BS antennas increase ASE, but the gain depends on the BS-user density ratio and on the number of antennas when user density is fixed. EE increases only when the increase in ASE dominates the increase of the power consumption per unit area. On the other hand, when the user density increases, ASE in interference-limited region can be improved by deploying more BS without sacrificing EE and the ergodic rate of the users
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B, da Silva Jr Jose Mairton. "Spectral Efficiency and Fairness Maximization in Full-Duplex Cellular Networks." Licentiate thesis, KTH, Nätverk och systemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-204607.
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Future cellular networks, the so-called 5G, are expected to provide explosive data volumes and data rates. To meet such a demand, the research communities are investigating new wireless transmission technologies. One of the most promising candidates is in-band full-duplex communications. These communications are characterized by that a wireless device can simultaneously transmit and receive on the same frequency channel. In-band full-duplex communications have the potential to double the spectral efficiencywhen compared to current half duplex systems. The traditional drawback of full-duplex was the interference that leaks from the own transmitter to its own receiver, the so- called self-interference, which renders the receiving signal unsuitable for communication.However, recent advances in self-interference suppression techniques have provided high cancellation and reduced the self-interference to noise floor levels, which shows full-duplex is becoming a realistic technology component of advanced wireless systems. Although in-band full-duplex promises to double the data rate of existing wireless technologies, its deployment in cellular networks is challenging due to the large number of legacy devices working in half-duplex. A viable introduction in cellular networks is offered by three-node full-duplex deployments, in which only the base stations are full-duplex, whereas the user- or end-devices remain half-duplex. However, in addition to the inherent self-interference, now the interference between users, the user-to-user interference, may become the performance bottleneck, especially as the capability to suppress self-interference improves. Due to this new interference situation, user pairing and frequency channel assignment become of paramount importance, because both mechanisms can help to mitigate the user-to-user interference. It is essential to understand the trade-offs in the performance of full-duplex cellular networks, specially three-node full-duplex, in the design of spectral and energy efficient as well as fair mechanisms. This thesis investigates the design of spectral efficient and fair mechanisms to improve the performance of full-duplex in cellular networks. The novel analysis proposed in this thesis suggests centralized and distributed user pairing, frequency channel assignment and power allocation solutions to maximize the spectral efficiency and fairness in future full-duplex cellular networks. The investigations are based on distributed optimization theory with mixed integer-real variables and novel extensions of Fast-Lipschitz optimization. The analysis sheds lights on two fundamental problems of standard cellular networks, namely the spectral efficiency and fairness maximization, but in the new context of full-duplex communications. The results in this thesis provide important understanding in the role of user pairing, frequency assignment and power allocation, and reveal the special behaviourbetween the legacy self-interference and the new user-to-user interference. This thesis can provide input to the standardization process of full-duplex communications, and have the potential to be used in the implementation of future full-duplex in cellular networks.QC 20170403
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Lo, Ernest Sze-Yuen. "Cooperation and multiuser resource allocation for spectral-efficient wireless networks /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?ECED%202008%20LO.
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Malmström, Josef, and Najib Yavari. "Power Spectral Density Based Sleep Scoring Using Artificial Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239371.
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Sleep scoring is the process that medical researchers use to analyze the sleep of a subject. By looking at signals in the brain and muscles, it is possible to determine the current sleep state of the subject. The procedure is traditionally done manually, requiring a lot of tedious processing of data. In this report, a machine learning system that automates the process of sleep scoring is studied and developed. The system works by estimating the power spectral density of the electroencephalography (EEG) and electromyography (EMG) signals, and training an artificial neural network to classify the correct sleep state. The signal processing was done in Python and the artificial neural network was implemented in Keras, using a TensorFlow back end. Finally, the implemented system proved to have an accuracy comparable to that of manual sleep scoring on five different rat datasets. Additionally, the system was able to generalize beyond the rat specimens it was trained on, meaning it could potentially be used on specimens that lack labeled sample data.
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Sun, Haijian. "Spectral, Energy and Computation Efficiency in Future 5G Wireless Networks." DigitalCommons@USU, 2019. https://digitalcommons.usu.edu/etd/7561.
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Wireless technology has revolutionized the way people communicate. From first generation, or 1G, in the 1980s to current, largely deployed 4G in the 2010s, we have witnessed not only a technological leap, but also the reformation of associated applications. It is expected that 5G will become commercially available in 2020. 5G is driven by ever-increasing demands for high mobile traffic, low transmission delay, and massive numbers of connected devices. Today, with the popularity of smart phones, intelligent appliances, autonomous cars, and tablets, communication demands are higher than ever, especially when it comes to low-cost and easy-access solutions.
Existing communication architecture cannot fulfill 5G’s needs. For example, 5G requires connection speeds up to 1,000 times faster than current technology can provide. Also, from transmitter side to receiver side, 5G delays should be less than 1ms, while 4G targets a 5ms delay speed. To meet these requirements, 5G will apply several disruptive techniques. We focus on two of them: new radio and new scheme. As for the former, we study the non-orthogonal multiple access (NOMA) and as for the latter, we use mobile edge computing (MEC).
Traditional communication systems allow users to communicate alternatively, which clearly avoids inter-user interference, but also caps the connection speed. NOMA, on the other hand, allows multiple users to transmit simultaneously. While NOMA will inevitably cause excessive interference, we prove such interference can be mitigated by an advanced receiver side technique. NOMA has existed on the research frontier since 2013. Since that time, both academics and industry professionals have extensively studied its performance. In this dissertation, our contribution is to incorporate NOMA with several potential schemes, such as relay, IoT, and cognitive radio networks. Furthermore, we reviewed various limitations on NOMA and proposed a more practical model.
In the second part, MEC is considered. MEC is a transformation from the previous cloud computing system. In particular, MEC leverages powerful devices nearby and instead of sending information to distant cloud servers, the transmission occurs in closer range, which can effectively reduce communication delay. In this work, we have proposed a new evaluation metric for MEC which can more effectively leverage the trade-off between the amount of computation and the energy consumed thereby.
A practical communication system for wearable devices is proposed in the last part, which combines all the techniques discussed above. The challenges for wearable communication are inherent in its diverse needs, as some devices may require low speed but high reliability (factory sensors), while others may need low delay (medical devices). We have addressed these challenges and validated our findings through simulations.
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Benigni, Matthew Curran. "Detection and Analysis of Online Extremist Communities." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/949.
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Online social networks have become a powerful venue for political activism. In many cases large, insular online communities form that have been shown to be powerful diffusion mechanisms of both misinformation and propaganda. In some cases these groups users advocate actions or policies that could be construed as extreme along nearly any distribution of opinion, and are thus called Online Extremist Communities (OECs). Although these communities appear increasingly common, little is known about how these groups form or the methods used to influence them. The work in this thesis provides researchers a methodological framework to study these groups by answering three critical research questions: How can we detect large dynamic online activist or extremist communities? What automated tools are used to build, isolate, and influence these communities? What methods can be used to gain novel insight into large online activist or extremist communities? These group members social ties can be inferred based on the various affordances offered by OSNs for group curation. By developing heterogeneous, annotated graph representations of user behavior I can efficiently extract online activist discussion cores using an ensemble of unsupervised machine learning methods. I call this technique Ensemble Agreement Clustering. Through manual inspection, these discussion cores can then often be used as training data to detect the larger community. I present a novel supervised learning algorithm called Multiplex Vertex Classification for network bipartition on heterogeneous, annotated graphs. This methodological pipeline has also proven useful for social botnet detection, and a study of large, complex social botnets used for propaganda dissemination is provided as well. Throughout this thesis I provide Twitter case studies including communities focused on the Islamic State of Iraq and al-Sham (ISIS), the ongoing Syrian Revolution, the Euromaidan Movement in Ukraine, as well as the alt-Right.
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Gkantsidis, Christos. "Algorithmic performance of large-scale distributed networks a spectral method approach /." Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-12062005-141254/.
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Thesis (Ph. D.)--Computing, Georgia Institute of Technology, 2006.Mihail, Milena, Committee Chair ; Ammar, Mostafa, Committee Member ; Dovrolis, Constantinos, Committee Member ; Faloutsos, Michalis, Committee Member ; Zegura, Ellen, Committee Member.
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Gkantsidis, Christos. "Algorithmic performance of large-scale distributed networks: A spectral method approach." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/10420.
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Complex networks like the Internet, peer-to-peer systems, and emerging sensor and ad-hoc networks are large distributed decentralized communication systems arising repeatedly in today's technology. In such networks it is critical to characterize network performance as the size of the network scales. The focus of my work is to relate basic network performance metrics to structural characteristics of underlying network topologies, and to develop protocols that reinforce and exploit desired structural characteristics. For the case of the Internet at the Autonomous System level, we relate the graph theoretic notions of conductance and spectrum to network clustering and network congestion. In particular, we show how spectral analysis can identify clusters, and how the presence of clusters affects congestion. This is important for network prediction and network simulation. For the case of peer-to-peer networks we relate conductance and spectral gap to the fundamental questions of searching and topology maintenance. We propose new protocols for maintaining peer-to-peer networks with good conductance and low network overhead. We compare the performance of the traditional method of search by flooding to searching by random walks. We isolate cases of practical interest, such as clustered and dynamic network topologies, where the latter have superior performance. The improvement in the performance can be directly quantified in terms of the conductance of the underlying topology. We introduce further hybrid search schemes, of which flooding and random walks are special instances, which aim to improve the performance of searching by using locally maintained information about the network topology.
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Preciado, Víctor Manuel. "Spectral analysis for stochastic models of large-scale complex dynamical networks." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45873.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 179-196).
Research on large-scale complex networks has important applications in diverse systems of current interest, including the Internet, the World-Wide Web, social, biological, and chemical networks. The growing availability of massive databases, computing facilities, and reliable data analysis tools has provided a powerful framework to explore structural properties of such real-world networks. However, one cannot efficiently retrieve and store the exact or full topology for many large-scale networks. As an alternative, several stochastic network models have been proposed that attempt to capture essential characteristics of such complex topologies. Network researchers then use these stochastic models to generate topologies similar to the complex network of interest and use these topologies to test, for example, the behavior of dynamical processes in the network. In general, the topological properties of a network are not directly evident in the behavior of dynamical processes running on it. On the other hand, the eigenvalue spectra of certain matricial representations of the network topology do relate quite directly to the behavior of many dynamical processes of interest, such as random walks, Markov processes, virus/rumor spreading, or synchronization of oscillators in a network. This thesis studies spectral properties of popular stochastic network models proposed in recent years. In particular, we develop several methods to determine or estimate the spectral moments of these models. We also present a variety of techniques to extract relevant spectral information from a finite sequence of spectral moments. A range of numerical examples throughout the thesis confirms the efficacy of our approach. Our ultimate objective is to use such results to understand and predict the behavior of dynamical processes taking place in large-scale networks.
by Víctor Manuel Preciado.
Ph.D.
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Lima, Francisco Rafael Marques. "Maximizing spectral efficiency under minimum satisfaction constraints on multiservice wireless networks." reponame:Repositório Institucional da UFC, 2012. http://www.repositorio.ufc.br/handle/riufc/5065.
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LIMA, F. R. M. Maximizing spectral efficiency under minimum satisfaction constraints on multiservice wireless networks. 2012. 124 f. Tese (Doutorado em Engenharia de Teleinformática) - Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2012.Submitted by Marlene Sousa (mmarlene@ufc.br) on 2013-06-17T13:18:17Z No. of bitstreams: 1 2012_tese_frmlima.pdf: 2443426 bytes, checksum: 15428eca4947de06979660cd40fdc53d (MD5)
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Cellular networks are now a new player in the competitive market of data service provision mainly due to the technological advances of 3rd Generation (3G) and the upcoming 4th Generation (4G). Long Term Evolution (LTE) and LTE-Advanced are examples of systems that are capable of providing high data rates to the end user. The need of being connected anytime and anywhere and the appealing mobile devices/applications are strong indications that the mobile broadband market has potential for further worldwide increasing. This new scenario with sophisticated mobile terminals enables the quick popularization of new appealing data mobile applications. As a consequence, it is expected that the traffic on mobile networks will have a considerable increase in the next years. Therefore, the sustainable Quality of Service (QoS) provision of heterogeneous services appears as a challenging scenario for mobile network operators and industry in the near future. In order to deal with this challenging scenario, improvements in the core network have been done by means of an Internet Protocol (IP)-based packet-switched architecture. In the radio access network, the use of Orthogonal Frequency Division Multiple Access (OFDMA) and Single Carrier - Frequency Division Multiple Access (SC-FDMA) as the multiple access schemes of downlink and uplink of LTE system, respectively, and the addition of multiple antennas techniques have boosted the achieved data rates in the radio part of the networks. Another relevant functionality that is useful to deal with the challenges of next generation cellular networks is efficient Radio Resource Allocation (RRA). RRA algorithms interact with multiple access schemes and multiple antenna schemes and are responsible for the management of the scarce radio resources such as power, time slots, spatial channels and frequency chunks. In this context, we study in this thesis the use of RRA in cellular networks in order to improve the resource usage efficiency and guarantee the sustainable provision of multiple services. More specifically, we model this RRA problem as the optimization problem of maximizing the overall data rate subject to minimum satisfaction constraints per service. Along this thesis, we study this problem in different scenarios with different multiple access strategies and multiple antennas schemes. As main contributions we provide the characterization of optimal solutions, proposal of low-complexity heuristic solutions, performance evaluation by means of computational simulations and computational complexity analysis of the involved algorithms.
Redes celulares entraram recentemente no competitivo mercado de provimento de serviços de dados devido principalmente aos avanços tecnológicos da terceira geração (3G) e da iminente quarta geração (4G). Os sistemas Long Term Evolution (LTE) e LTE-Advanced são exemplos de redes celulares que proporcionam altas taxas de dados a seus usuários. A necessidade de estar conectado de forma permanente e os novos e poderosos dispositivos móveis são fortes indicadores que o mercado de banda larga móvel ainda possui potencial de crescimento em nível global. Este novo cenário com sofisticados dispositivos móveis permite a rápida popularização de novas aplicações de dados móveis. Como consequência, esperamos que o tráfego nas redes móveis tenham um aumento considerável nos próximos anos. Portanto, o provimento de Qualidade de Serviço (do inglês, Quality of Service (QoS)) para serviços heterogêneos consiste em um cenário desafiador para os operadores dos sistemas e indústria em um futuro próximo. De forma a enfrentar esses desafios, algumas melhorias foram realizadas no núcleo da rede por meio do advento da arquitetura por chaveamento por pacotes baseado em protocolo da internet (do inglês, Internet Protocol (IP)). Na rede de acesso de rádio, tivemos como avanços o uso de múltiplas antenas nos nós da rede e a adoção dos esquemas de múltiplo acesso por divisão de frequências ortogonais (do inglês, Orthogonal Frequency Division Multiple Access (OFDMA)) e múltiplo acesso por divisão de frequências com portadora única (do inglês, Single Carrier - Frequency Division Multiple Access (SC-FDMA)) nos enlaces direto e reverso do sistema LTE, respectivamente. Outra funcionalidade que destacamos como relevante para enfrentar os desafios das próximas gerações de redes celulares consiste no uso de alocação de recursos de rádio (do inglês, Radio Resource Allocation (RRA)). Algoritmos de RRA são responsáveis pelo gerenciamento dos recursos de rádio tais como intervalos de tempo (do inglês, time slots), canais espaciais e grupos de frequências que em geral são escassos. Neste contexto, nós estudamos nesta tese o uso de RRA em redes celulares de forma a melhorar a eficiência no uso dos recursos e garantir um provimento sustentável de múltiplos serviços. Especificamente, modelamos RRA como o problema de otimização de maximização da taxa total de transmissão sujeito a restrições de satisfação mínimas por serviço. Este problema é estudado ao longo da tese em diferentes cenários resultantes da combinação de diferentes esquemas de múltiplo acesso e múltiplas antenas. Como principais contribuições temos a caracterização de soluções ótimas, propostas de heurísticas de baixa complexidade, avaliação de desempenho por meio de simulações computacionais e por fim a análise da complexidade dos algoritmos envolvidos.
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Le, Nam-Tran. "Spectral and energy efficient resource allocation in OFDMA Femtocell heterogeneous networks." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/132152/1/__qut.edu.au_Documents_StaffHome_staffgroupW%24_wu75_Documents_ePrints_Nam-Tran_Le_Thesis_Redacted.pdf.
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This thesis presents novel resource allocation algorithms for energy and power efficient transmissions in future mobile wireless networks. Algorithms presented in thesis incorporate a combination of practical conditions: mixed services, rate fairness, the density of deployment of femtocells and non-linear power consumption. The resource allocation problem of sparse and dense deployment of Orthogonal Frequency Division Multiple Access based heterogeneous femtocell networks is solved by optimising spectral efficiency, energy efficiency and spectral and energy efficiency trade-off respectively. In general, this research proposes several resource allocation algorithms that are practical, efficient and lower in computational complexity.
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Chakraborty, Pratik. "Improving secrecy and spectral utilization efficiency in networks with underlay cognitive nodes." Thesis, IIT Delhi, 2018. http://eprint.iitd.ac.in:80//handle/2074/8043.
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Onireti, Oluwakayode S. "Fundamental trade-off between energy efficiency and spectral efficiency in cellular networks." Thesis, University of Surrey, 2012. http://epubs.surrey.ac.uk/810838/.
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In a context of energy saving and operational cost reduction, energy efficiency (EE) has emerged as an important performance metric in cellular networks. According to the famous Shannon’s capacity theorem, maximising the EE while maximising the spectral efficiency (SE) are conflicting objectives, hence, both metrics can be jointly studied via their trade-off, i.e. the EE-SE trade-off. In this context, the aim of this thesis is to investigate the fundamental trade-off between EE and SE in futuristic cellular networks where distributed multiple-input multiple output (DMIMO) or coordinated multi-point (CoMP) scheme is utilised for meeting the high data rate of the next generation wireless communication networks. Focusing on the DMIMO system, a novel tight closed-form approximation of its EE-SE trade-off is derived and its accuracy verified for both the uplink and downlink channels and for both the idealistic and realistic power consumption models (PCMs). In addition, the low and high-SE regime approximations of the DMIMO EE-SE trade-off are derived in the uplink and downlink channels. Furthermore, these approximations are utilised for assessing both the EE gain of the DMIMO over the co-located MIMO (CMIMO) and the incremental EE gain of DMIMO in the downlink channel. It is observed that DMIMO is more energy efficient than CMIMO for cell edge users in both PCMs; whereas the results for realistic incremental EE gain indicate that the optimal approach in terms of EE is to connect the user terminal to only one radio access unit. Focusing on the uplink of the CoMP system, a generic closed-form approximation of the EE-SE trade-off is derived and its accuracy is demonstrated for both the idealistic and realistic PCMs. Asymptotic approximations of this trade-off in both the low and high SE regimes are also presented. Furthermore, these approximations are utilised to compare the EE of the CoMP system with the EE of the traditional non-cooperative system with orthogonal multiple access scheme. It is observed that in the idealistic PCM, CoMP is more energy efficient than the non-cooperative system due to a reduction in power consumption; whereas in the realistic PCM, CoMP can also be more energy efficient but due to an improvement in SE and mainly for cell-edge communication and small cell deployment.
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Chen, Zhiqian. "Graph Neural Networks: Techniques and Applications." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99848.
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Effective information analysis generally boils down to the geometry of the data represented by a graph. Typical applications include social networks, transportation networks, the spread of epidemic disease, brain's neuronal networks, gene data on biological regulatory networks, telecommunication networks, knowledge graph, which are lying on the non-Euclidean graph domain. To describe the geometric structures, graph matrices such as adjacency matrix or graph Laplacian can be employed to reveal latent patterns. This thesis focuses on the theoretical analysis of graph neural networks and the development of methods for specific applications using graph representation. Four methods are proposed, including rational neural networks for jump graph signal estimation, RemezNet for robust attribute prediction in the graph, ICNet for integrated circuit security, and CNF-Net for dynamic circuit deobfuscation.
For the first method, a recent important state-of-art method is the graph convolutional networks (GCN) nicely integrate local vertex features and graph topology in the spectral domain. However, current studies suffer from drawbacks: graph CNNs rely on Chebyshev polynomial approximation which results in oscillatory approximation at jump discontinuities since Chebyshev polynomials require degree $Omega$(poly(1/$epsilon$)) to approximate a jump signal such as $|x|$. To reduce complexity, RatioanlNet is proposed to integrate rational function and neural networks for graph node level embeddings. For the second method, we propose a method for function approximation which suffers from several drawbacks: non-robustness and infeasibility issue; neural networks are incapable of extracting analytical representation; there is no study reported to integrate the superiorities of neural network and Remez. This work proposes a novel neural network model to address the above issues. Specifically, our method utilizes the characterizations of Remez to design objective functions. To avoid the infeasibility issue and deal with the non-robustness, a set of constraints are imposed inspired by the equioscillation theorem of best rational approximation. The third method proposes an approach for circuit security. Circuit obfuscation is a recently proposed defense mechanism to protect digital integrated circuits (ICs) from reverse engineering. Estimating the deobfuscation runtime is a challenging task due to the complexity and heterogeneity of graph-structured circuit, and the unknown and sophisticated mechanisms of the attackers for deobfuscation. To address the above-mentioned challenges, this work proposes the first graph-based approach that predicts the deobfuscation runtime based on graph neural networks. The fourth method proposes a representation for dynamic size of circuit graph. By analyzing SAT attack method, a conjunctive normal form (CNF) bipartite graph is utilized to characterize the complexity of this SAT problem. To overcome the difficulty in capturing the dynamic size of the CNF graph, an energy-based kernel is proposed to aggregate dynamic features.Doctor of Philosophy
Graph data is pervasive throughout most fields, including pandemic spread network, social network, transportation roads, internet, and chemical structure. Therefore, the applications modeled by graph benefit people's everyday life, and graph mining derives insightful opinions from this complex topology. This paper investigates an emerging technique called graph neural newton (GNNs), which is designed for graph data mining. There are two primary goals of this thesis paper: (1) understanding the GNNs in theory, and (2) apply GNNs for unexplored and values real-world scenarios. For the first goal, we investigate spectral theory and approximation theory, and a unified framework is proposed to summarize most GNNs. This direction provides a possibility that existing or newly proposed works can be compared, and the actual process can be measured. Specifically, this result demonstrates that most GNNs are either an approximation for a function of graph adjacency matrix or a function of eigenvalues. Different types of approximations are analyzed in terms of physical meaning, and the advantages and disadvantages are offered. Beyond that, we proposed a new optimization for a highly accurate but low efficient approximation. Evaluation of synthetic data proves its theoretical power, and the tests on two transportation networks show its potentials in real-world graphs. For the second goal, the circuit is selected as a novel application since it is crucial, but there are few works. Specifically, we focus on a security problem, a high-value real-world problem in industry companies such as Nvidia, Apple, AMD, etc. This problem is defined as a circuit graph as apply GNN to learn the representation regarding the prediction target such as attach runtime. Experiment on several benchmark circuits shows its superiority on effectiveness and efficacy compared with competitive baselines. This paper provides exploration in theory and application with GNNs, which shows a promising direction for graph mining tasks. Its potentials also provide a wide range of innovations in graph-based problems.
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Bellam, Venkata Pavan Kumar. "Efficient Community Detection for Large Scale Networks via Sub-sampling." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/81862.
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Many real-world systems can be represented as network-graphs. Some of the networks have an inherent community structure based on interactions. The problem of identifying this grouping structure given a graph is termed as community detection problem which has certain existing algorithms. This thesis contributes by providing specific improvements to various community detection algorithms such as spectral clustering and extreme point algorithm. One of the main contributions is proposing a new sub-sampling method to make existing spectral clustering method scalable by reducing the computational complexity. Also, we have implemented extreme points algorithm for a general multiple communities detection case along with a sub-sampling based version to reduce the computational complexity. We have also developed spectral clustering algorithm for popularity-adjusted block model (PABM) model based graphs to make the algorithm exact thus improving its accuracy.Master of Science
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Staufer-Steinnocher, Petra, and Manfred M. Fischer. "A Neural Network Classifier for Spectral Pattern Recognition. On-Line versus Off-Line Backpropagation Training." WU Vienna University of Economics and Business, 1997. http://epub.wu.ac.at/4152/1/WSG_DP_6097.pdf.
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In this contributon we evaluate on-line and off-line techniques to train a single
hidden layer neural network classifier with logistic hidden and softmax output transfer
functions on a multispectral pixel-by-pixel classification problem. In contrast to
current practice a multiple class cross-entropy error function has been chosen as the
function to be minimized. The non-linear diffierential equations cannot be solved in
closed form. To solve for a set of locally minimizing parameters we use the gradient
descent technique for parameter updating based upon the backpropagation technique
for evaluating the partial derivatives of the error function with respect to the
parameter weights. Empirical evidence shows that on-line and epoch-based gradient
descent backpropagation fail to converge within 100,000 iterations, due to the fixed
step size. Batch gradient descent backpropagation training is superior in terms of
learning speed and convergence behaviour. Stochastic epoch-based training tends to
be slightly more effective than on-line and batch training in terms of generalization
performance, especially when the number of training examples is larger. Moreover, it
is less prone to fall into local minima than on-line and batch modes of operation. (authors' abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScience
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Xiong, Cong. "Energy-efficient design in wireless communications networks." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52217.
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The widespread application of wireless services and the requirements of ubiquitous access have recently triggered rapidly booming energy consumption in wireless communications networks. Such escalation of energy consumption in wireless networks causes high operational expenditure from electricity bills for operators, unsatisfactory user experience due to limited battery capacity of wireless devices, and a large amount of greenhouse gas emission. Green radio (GR), which emphasizes both energy efficiency (EE) and spectral efficiency (SE), has been proposed as an effective solution and is becoming the mainstream for future wireless network design. Unfortunately, EE and SE do not always coincide and may even sometimes conflict. In this dissertation, we focus on energy-efficient transmission and resource allocation techniques for orthogonal frequency division multiple access (OFDMA) networks and the joint energy-efficient design of OFDMA and other promising wireless communications techniques, such as cognitive radio (CR) and two-way relay.
Firstly, we investigate the principles of energy-efficient design for pure OFDMA networks. As the first step, we study the fundamental interrelationship between EE and SE in downlink OFDMA networks and analyze the impacts of channel gain and circuit power on the EE-SE relationship. We establish a general EE-SE optimization framework, where the overall EE, SE and per-user
quality-of-service (QoS) are all considered. Under this framework, we find that EE is quasiconcave in SE and decreases with SE when SE is large enough. These findings are very helpful guidelines for designing energy- and spectral-efficient OFDMA. To facilitate the application of energy-efficient resource allocation, we then investigate the energy-efficient resource allocation in both downlink and uplink OFDMA networks. For the downlink transmission, the generalized EE is maximized while for the uplink case the minimum individual EE is
maximized, both under prescribed per-user minimum data rate requirements. For both transmission scenarios, we first provide the optimal solution and then develop an computationally efficient suboptimal approach by exploring the inherent structure and property of the energy-efficient design. Then we study energy-efficient design in downlink OFDMA networks with effective capacity-based delay provisioning for delay-sensitive traffic. By integrating information theory with the concept of effective capacity, we formulate and solve an EE optimization problem with statistical delay provisioning. We also analyze the tradeoff between EE and delay, the relationship between spectral-efficient and energy-efficient designs, and the impact of system parameters, including circuit power and delay exponents, on the overall performance.
Secondly, we consider joint energy-efficient design of OFDMA and CR and two-way relay, respectively, to further enhance the EE and SE of wireless networks. We study energy-efficient opportunistic spectrum access strategies for an OFDMA-based CR network with multiple secondary users (SUs). Both worst EE and average EE of the SUs are considered and optimized subject to constraints including maximum transmit power and maximum interference to primary user (PU) system. For both cases, we first find the optimal solution and then propose a low-complexity suboptimal alternative. The results show that the energy-efficient CR strategies significantly boost EE compared with the conventional spectral-efficient CR ones while the low-complexity suboptimal approaches can well balance the performance and complexity. Then we study energy-efficient resource allocation for OFDMA-based two-way relay, which aims at maximizing the aggregated EE utility while provisioning proportional fairness in EE among different terminal pairs. Different from most exist energy-efficient design, we consider a new circuit power model, where the dynamic circuit power is proportional to the number of active subcarrier. For low-complexity solution, we propose an EE-oriented sequential subchannel assignment policy and discover the sufficient condition for early termination of the sequential subchannel assignment without losing the EE optimality. It is found that the energy-efficient transmission does not necessarily make all the subcarriers active, which is another useful principle for practical energy-efficient system design.
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Zhou, Bingxin. "Geometric Signal Processing with Graph Neural Networks." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28617.
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One of the most predominant techniques that have achieved phenomenal success in many modern applications is deep learning. The obsession with massive data analysis in image recognition, speech processing, and text understanding spawns remarkable advances in deep learning of diverse research areas. The alliance of deep learning technologies yields mighty graph neural networks (GNNs), an emerging type of deep neural networks that encodes internal structural relationships of inputs. The mainstream of GNNs finds an adequate numerical representation of graphs, which is vital to the prediction performance of machine learning models. Graph representation learning has many real-world applications, such as drug repurposing, protein classification, epidemic spread controlling, and social networks analysis. The rapid development of GNNs in the last five years has witnessed a couple of design flaws, such as over-smoothing, vulnerability to perturbation, lack of expressivity, and missing explainability. Meanwhile, the persistent enthusiasm in this research area allows for cumulative experience in solving complicated problems, such as size-variant graph compression and time-variant graph dynamic capturing.
The ambition of this thesis is to shed some light of mathematics on a few outlined issues. The permutation-invariant design of graph compression is supported by manifold learning, the robust graph smoothing relies heavily on the principles of convex optimization, and the efficient dynamic graph embedding leverages global spectral transforms and power method singular value decomposition. The author believes that the effectiveness of deep learning designs should not be oriented solely by performance over particular datasets. Modifications on a black-box model should operate beyond fine-tuning tricks. The reliability of deep learning looks forward to designing models with rigorous mathematics so that the `computer science' becomes actual science one day.
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Franke, Victoria Eleanor. "One-dimensional spectral/hp element simulation of wave propagation in human arterial networks." Thesis, Imperial College London, 2003. http://hdl.handle.net/10044/1/7221.
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Alhashim, Najeeb S. "Performance of Disparate-Bandwidth DS-SS Systems in Spectral Overlay Ad Hoc Networks." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1243668702.
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Kassa, Hailu Belay, Tewelgn Kebede Engda, and Estifanos Yohannes Menta. "EVALUATION OF SPECTRAL Vs ENERGY EFFICIENCY TRADEOFF CONSIDERING TRANSMISSION RELIABILITY IN CELLULAR NETWORKS." International Foundation for Telemetering, 2016. http://hdl.handle.net/10150/624224.
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Spectral efficiency (SE), energy efficiency (EE), and transmission reliability are basic
parameters to measure the performance of a cellular network. In this paper, spectral
efficiency and energy efficiency tradeoff is considered keeping in mind the transmission
reliability, where all the three are function of signal to noise ratio (SNR). SNR, in turn is a
function of constellation size (or the number of bits per symbol) and data rate. Then, we
propose a new power model which is as function of this SNR. Based on the power model,
SE-EE trade-off function is evaluated taking transmission reliability in to consideration.
Results confirmed that increasing constellation size results an increase in SNR and leads to a
significant increase in energy efficiency without changing the transmit power. To
demonstrate the validity of our analysis, channel gain and constellation size are varied
keeping transmit power constant. The results also indicate that securing transmission
reliability, the EE-SE trade-off is optimized by increasing the constellation size.
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Mugume, Edwin. "Green heterogeneous cellular networks." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/green-heterogeneous-cellular-networks(e7976a91-c891-4174-abaf-18820ff1736d).html.
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Data traffic demand has been increasing exponentially and this trend will continue over theforeseeable future. This has forced operators to upgrade and densify their mobile networks toenhance their capacity. Future networks will be characterized by a dense deployment of different kinds of base stations (BSs) in a hierarchical cellular structure. However network densification requires extensive capital and operational investment which limits operator revenues and raises ecological concerns over greenhouse gas emissions. Although networks are planned to support peak traffic, traffic demand is actually highly variable in both space and time which makes it necessary to adapt network energy consumption to inevitable variations in traffic demand. In this thesis, stochastic geometry tools are used to perform simple and tractable analysis of thecoverage, rate and energy performance of homogeneous networks and heterogeneous networks(HetNets). BSs in each tier are located according to independent Poisson Point Processes(PPPs) to generate irregular topologies that fairly resemble practical deployment topologies. The homogeneous network is optimized to determine the optimal BS density and transmit power configuration that minimizes its area power consumption (APC) subject to both coverage and average rate constraints. Results show that optimal transmit power only depends on the BSpower consumption parameters and can be predetermined. Furthermore, various sleep modemechanisms are applied to the homogeneous network to adapt its APC to changes in userdensity. A centralized strategic scheme which prioritize BSs with the least number of usersenhances energy efficiency (EE) of the network. Due to the complexity of such a centralizedscheme, a distributed scheme which implements the strategic algorithm within clusters of BSsis proposed and its performance closely matches that of its centralized counterpart. It is more challenging to model the optimal deployment configuration per tier in a multi-tier HetNet. Appropriate assumptions are used to determine tight approximations of these deployment configurations that minimize the APC of biased and unbiased HetNets subject tocoverage and rate constraints. The optimization is performed for three different user associationschemes. Similar to the homogeneous network, optimal transmit power per tier also depends onBS power consumption parameters only and can also be predetermined. Analysis of the effect of biasing on HetNet performance shows appropriate biasing can further reduce the deploymentconfiguration (and consequently the APC) compared to an unbiased HetNet. In addition, biasing can be used to offload traffic from congesting and high-power macro BSs to low-power small BSs. If idle BSs are put into sleep mode, more energy is saved and HetNet EE improves. Moreover, appropriate biasing also enhances the EE of the HetNet.
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Almelah, Hisham Bashir. "Design and analysis of next generation MIMO networks." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/design-and-analysis-of-next-generation-mimo-networks(43d00dbb-5b2d-478f-b0dc-eb0503c5167a).html.
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Spectral efficiency is one of the most important measures of the performance of wireless communication systems owing to scarcity and cost of the radio spectrum. The increase in spectral efficiency provides higher data rates to the user, lower network cost to the operator, coverage extension and higher service reliability as well. Intercell interference due to frequency reuse is one of the key impairments in wireless systems. Multiple-input multiple-output (MIMO) technique has been developed to enhance the desired signal power (and hence mitigating the effects of intercell interference) and with employing simple linear signal processing technique, can strongly mitigate the interference resulting from co-channel users. This technique is mainly used to achieve spatial diversity for boosting the communication link reliability by combating fading, and providing spatial multiplexing to increase data rates without extra bandwidth by exploiting multipath. Distributed antenna system (DAS) has recently gained substantial interest due to its ability to reduce transmitted power thereby lowering the out-of-cell interference effects, maximise the coverage and improve the spectral efficiency. The combination of MIMO techniques with DAS, so-called distributed MIMO (D-MIMO) systems, is now being exploited and largely succeeded to fulfil the services of the fourth generation (4G) wireless systems. Very recently, one of crucial significance approach to reducing the radiated power and improving spectral efficiency to cope with fifth generation (5G) wireless systems is the use of large-scale MIMO (also referred to as massive MIMO) technology, which utilizes a large number of antennas, i.e., tens to hundreds, typically at the base station (BS) side. Presently, in the light of the rapid evolution of wireless systems into 5G, the integration of wireless power transfer (WPT) with newly wireless systems has seen a great deal of attention as a potential solution for powering energy-constrained wireless systems, especially with shortening communication links by emerging new technologies, e.g., D-MIMO and massive MIMO. This thesis is devoted to investigating and comparing the performance of three different MIMO systems. More specifically, the thesis focuses on analysing the spectral efficiency of a comprehensive model of self-powered MU-MIMO systems employing linear ZF technique at the BS for both perfect and imperfect channel state information (CSI) cases. The results demonstrate the impact of practical channel impairments, e.g., spatial correlation, shadowing and co-channel interference (CCI), and system parameters, e.g., the number of BS and user antennas, signal to noise ratio (SNR) and channel estimation error, on the spectral efficiency of the system. Besides, from a spectral efficiency perspective, a proposed model of a combination of MIMO and massive MIMO technologies with DAS in the presence of linear receivers at the processing unit (PU) is considered and compared to a centralised MIMO (CMIMO) system. The obtained results provide a wide range of insights into the effects of system parameters on the spectral efficiency and reveal that the proposed distributed MIMO system outperforms the C-MIMO system. In the context of wireless powered MIMO systems, this work investigates the performance of a power beacon (PB)-assisted wireless powered C-MIMO system, including one multi-antenna BS and a number of single-antenna users powered by randomly deployed PBs in the presence of ZF receiver at the BS. Also, two modes for radiation from the PBs are assumed and compared, one is the beamforming radiation mode (BRM), and the other is the isotropic radiation mode (IRM).
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Tso, Brandt C. K. "An investigation of alternative strategies for incorporating spectral, textural, and contextual information in remote sensing image classification." Thesis, University of Nottingham, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387663.
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Erdem, Ozge. "Computation And Analysis Of Spectra Of Large Undirected Networks." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612233/index.pdf.
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Many interacting complex systems in biology, in physics, in technology and social systems, can be represented in a form of large networks. These large networks are mathematically represented by graphs. A graph is represented usually by the adjacency or the Laplacian matrix. Important features of the underlying structure and dynamics of them
can be extracted from the analysis of the spectrum of the graphs. Spectral analysis of the so called normalized Laplacian of large networks became popular in the recent years. The Laplacian matrices of the empirical networks are in form of unstructured large sparse matrices. The aim of this thesis is the comparison of different eigenvalue solvers for large sparse symmetric matrices which arise from the graph theoretical epresentation of undirected networks. The spectrum of the
normalized Laplacian is in the interval [0 2] and the multiplicity of the eigenvalue 1 plays a particularly important role for the network analysis. Moreover, the spectral analysis of protein-protein interaction networks has revealed that these networks have a different distribution type than other model networks such as scale free networks. In this respect, the eigenvalue solvers implementing the well-known implicitly
restarted Arnoldi method, Lanczos method, Krylov-Schur and Jacobi Davidson methods are investigated. They exist as MATLAB routines and are included in some freely available packages. The performances of different eigenvalue solvers PEIG, AHBEIGS, IRBLEIGS, EIGIFP, LANEIG, JDQR, JDCG in MATLAB and the library SLEPc in C++ were tested for matrices of size between 100-13000 and are compared in
terms of accuracy and computing time. The accuracy of the eigenvalue solvers are validated for the Paley graphs with known eigenvalues and are compared for large empirical networks using the residual plots and spectral density plots are computed.
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Pervaiz, Haris Bin. "Optimising energy efficiency and spectral efficiency in multi-tier heterogeneous networks : performance and tradeoffs." Thesis, Lancaster University, 2016. http://eprints.lancs.ac.uk/80695/.
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The exponential growth in the number of cellular users along with their increasing demand of higher transmission rate and lower power consumption is a dilemma for the design of future generation networks. The spectral efficiency (SE) can be improved by better utilisation of the network resources at the cost of reduction in the energy efficiency (EE) due to the enormous increase in the network power expenditure arising from the densification of the network. One of the possible solutions is to deploy Heterogeneous Networks (HetNets) consisting of several tiers of small cell BSs overlaid within the coverage area of the macrocells. The HetNets can provide better coverage and data rate to the cell edge users in comparison to the macrocells only deployment. One of the key requirements for the next generation networks is to maintain acceptable levels of both EE and SE. In order to tackle these challenges, this thesis focuses on the analysis of the EE, SE and their tradeoff for different scenarios of HetNets. First, a joint network and user adaptive selection mechanism in two-tier HetNets is proposed to improve the SE using game theory to dynamically re-configure the network while satisfying the user's quality-of-service (QoS) requirements. In this work, the proposed scheme tries to offload the traffic from the heavily loaded small cells to the macrocell. The user can only be admitted to a network which satisfies the call admission control procedures for both the uplink and downlink transmission scheme. Second, an energy efficient resource allocation scheme is designed for a two-tier HetNets. The proposed scheme uses a low-complexity user association and power allocation algorithm to improve the uplink system EE performance in comparison to the traditional cellular systems. In addition, an opportunistic joint user association and power allocation algorithm is proposed in an uplink transmission scheme of device to device (D2D) enabled HetNets. In this scheme, each user tries to maximise its own Area Spectral Efficiency (ASE) subject to the required Area Energy Efficiency (AEE) requirements. Further, a near-optimal joint user association and power allocation approach is proposed to investigate the tradeoff between the two conflicting objectives such as achievable throughput and minimising the power consumption in two-tier HetNets for the downlink transmission scheme. Finally, a multi-objective optimization problem is formulated that jointly maximizes the EE and SE in two-tier HetNets. In this context, a joint user association and power allocation algorithm is proposed to analyse the tradeoff between the achievable EE and SE in two-tier HetNets. The formulated problem is solved using convex optimisation methods to obtain the Pareto-optimal solution for the various network parameters.
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Parthasarathy, Gayathri. "Automated license plate recognition a novel approach using spectral analysis and majority vote neural networks /." abstract and full text PDF (free order & download UNR users only), 2006. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1433377.
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Habib, Christian. "All-optical label swapping strategies for spectral amplitude code labels in packet-switched optical networks." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=116005.
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There is currently much work focused on developing packet-switched optical networks to overcome the limitations of existing optical networks. Switch design for packet-switched optical networks is particularly challenging, in part due to the lack of a practical optical memory system. As a result, optical labels and all-optical label processing have attracted much attention. This thesis examines a crucial label processing component of an optical packet switch, namely the label swapper.In this thesis, three different tabletop topologies for low-cost all-optical swapping of spectral amplitude code labels for packet-switched networks are examined in a proof-of-concept phase. The first uses cross-absorption modulation in an electro-absorption modulator within a semiconductor fiber ring laser (SFRL), the second uses cross-gain modulation (XGM) in a semiconductor optical amplifier (SOA) within an SFRL, and the third makes use of XGM in a SOA as well as injection locking in a Fabry-Perot laser diode for wavelength conversion. The benefits and limitations of each approach as well as future improvements are discussed. Building on these results, a high-performance integrated version of XGM swapper is designed, simulated, and masks are produced for fabrication using indium phosphide technology.
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Karaputugala, Gamacharige Madushan Thilina. "On spectrum sensing, resource allocation, and medium access control in cognitive radio networks." Elsevier, 2012. http://hdl.handle.net/1993/30650.
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The cognitive radio-based wireless networks have been proposed as a promising technology
to improve the utilization of the radio spectrum through opportunistic spectrum access. In
this context, the cognitive radios opportunistically access the spectrum which is licensed to
primary users when the primary user transmission is detected to be absent. For opportunistic
spectrum access, the cognitive radios should sense the radio environment and allocate
the spectrum and power based on the sensing results. To this end, in this thesis, I develop
a novel cooperative spectrum sensing scheme for cognitive radio networks (CRNs) based
on machine learning techniques which are used for pattern classification. In this regard,
unsupervised and supervised learning-based classification techniques are implemented for
cooperative spectrum sensing. Secondly, I propose a novel joint channel and power allocation
scheme for downlink transmission in cellular CRNs. I formulate the downlink
resource allocation problem as a generalized spectral-footprint minimization problem. The
channel assignment problem for secondary users is solved by applying a modified Hungarian
algorithm while the power allocation subproblem is solved by using Lagrangian
technique. Specifically, I propose a low-complexity modified Hungarian algorithm for subchannel
allocation which exploits the local information in the cost matrix. Finally, I propose
a novel dynamic common control channel-based medium access control (MAC) protocol
for CRNs. Specifically, unlike the traditional dedicated control channel-based MAC protocols,
the proposed MAC protocol eliminates the requirement of a dedicated channel for
control information exchange.October 2015
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Yassin, Mohamad. "Inter-cell interference coordination in wireless networks." Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1S106/document.
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Grâce aux avancées technologiques dans le domaine des réseaux cellulaires et des équipements mobiles, le nombre d'applications multimédia à haut débit dans les réseaux mobiles ne cesse d'augmenter. On prévoit que le trafic de données dans les réseaux mobiles en 2017 sera 13 fois plus important que celui en 2012. Pour satisfaire aux besoins des équipements mobiles, de nouvelles approches pour la gestion des ressources radio et des puissances de transmission sont requises.Dans le cadre de cette thèse, on s'intéresse à proposer des solutions pour remédier aux problèmes des interférences intercellulaires dans les réseaux mobiles de dernière génération. Nous enquêtons d'une manière exhaustive les différentes techniques de coordination des interférences intercellulaires existantes. Ces techniques sont qualitativement comparées, puis classées selon le taux de coopération requis entre les différentes stations de base, mais aussi selon leurs principes de fonctionnement. Nous abordons également le problème multicellulaire d'allocation des ressources et des puissances de transmission d'une manière centralisée. Nous formulons ce problème d'optimisation centralisé, puis nous le décomposons en deux sous-problèmes indépendants : l'allocation de ressources et l'allocation des puissances de transmission. De plus, une approche distribuée basée sur la théorie des jeux est proposée pour l'allocation des puissances de transmission. Les techniques centralisées de minimisation des interférences intercellulaires offrent la solution optimale au prix d'une grande charge de signalisation. Par contre, les solutions décentralisées réduisent le trafic de signalisation sans garantir l'optimalité de la solution obtenue. Nous proposons ensuite une heuristique de contrôle de puissance qui modifie localement l'allocation des puissances de transmission de manière à éviter le gaspillage d'énergie et pour réduire les interférences ressenties par les utilisateurs des stations de base voisines. Nous proposons également une technique autonome qui gère la distribution des ressources radio entre les différentes zones de chaque cellule. Cette technique répond aux besoins des utilisateurs dans chaque zone en adaptant la distribution des ressources d'une manière dynamique. Nous abordons aussi le compromis entre les techniques de gestion d'interférences intercellulaires centralisées et décentralisées. Nous proposons une approche hybride où l'allocation des ressources radio et des puissances de transmission est faite d'une manière coopérative entre les différentes cellules. Dans un premier lieu, les cellules voisines collaborent afin d'ajuster les puissances de transmission allouées aux ressources radio. Ensuite, la distribution des ressources entre les différentes zones de chaque cellule est modifiée localement, selon les besoins des utilisateurs dans chaque zoneThe exponentially increasing demand for mobile broadband communications have led to the dense deployment of cellular networks with aggressive frequency reuse patterns. The future Fifth Generation (5G) networks are expected to overcome capacity and throughput challenges by adopting a multi-tier architecture where several low-power Base Stations (BSs) are deployed within the coverage area of the macro cell. However, Inter-Cell Interference (ICI) caused by the simultaneous usage of the same spectrum in different cells, creates severe problems. ICI reduces system throughput and network capacity, and has a negative impact on cell-edge User Equipment (UE) performance. Therefore, Inter-Cell Interference Coordination (ICIC) techniques are required to mitigate the impact of ICI on system performance. In this thesis, we address the resource and power allocation problem in multiuser Orthogonal Frequency Division Multiple Access (OFDMA) networks such as LTE/LTE-A networks and dense small cell networks. We start by overviewing the state-of-the-art schemes, and provide an exhaustive classification of the existing ICIC approaches. This qualitative classification is followed by a quantitative investigation of several interference mitigation techniques. Then, we formulate a centralized multi-cell joint resource and power allocation problem, and prove that this problem is separable into two independent convex optimization problems. The objective function of the formulated problem consists in maximizing system throughput while guaranteeing throughput fairness between UEs. ICI is taken into account, and resource and power allocation is managed accordingly in a centralized manner. Furthermore, we introduce a decentralized game-theoretical method to solve the power allocation problem without the need to exchange signaling messages between the different cells. We also propose a decentralized heuristic power control algorithm based on the received Channel Quality Indication (CQI) feedbacks. The intuition behind this algorithm is to avoid power wastage for UEs that are close to the serving cell, and reducing ICI for UEs in the neighboring cells. An autonomous ICIC scheme that aims at satisfying throughput demands in each cell zone is also introduced. The obtained results show that this technique improves UE throughput fairness, and it reduces the percentage of unsatisfied UEs without generating additional signaling messages. Lastly, we provide a hybrid ICIC scheme as a compromise between the centralized and the decentralized approaches. For a cluster of adjacent cells, resource and power allocation decisions are made in a collaborative manner. First, the transmission power is adjusted after receiving the necessary information from the neighboring cells. Second, resource allocation between cell zones is locally modified, according to throughput demands in each zone
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Ekhtiari, Amiri Sorour. "Task-specific summarization of networks: Optimization and Learning." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/100993.
Full textAbstract:
Networks (also known as graphs) are everywhere. People-contact networks, social networks, email communication networks, internet networks (among others) are examples of graphs in our daily life. The increasing size of these networks makes it harder to understand them. Instead, summarizing these graphs can reveal key patterns and also help in sensemaking as well as accelerating existing graph algorithms. Intuitively, different summarizes are desired for different purposes. For example, to stop viral infections, one may want to find an effective policy to immunize people in a people-contact network. In this case, a high-quality network summary should highlight roughly structurally important nodes. Others may want to detect communities in the same people-contact network, and hence, the summary should show cohesive groups of nodes. This implies that for each task, we should design a specific method to reveal related patterns. Despite the importance of task-specific summarization, there has not been much work in this area.
Hence, in this thesis, we design task-specific summarization frameworks for univariate and multivariate networks. We start with optimization-based approaches to summarize graphs for a particular task and finally propose general frameworks which automatically learn how to summarize for a given task and generalize it to similar networks.
1. Optimization-based approaches: Given a large network and a task, we propose summarization algorithms to highlight specific characteristics of the graph (i.e., structure, attributes, labels, dynamics) with respect to the task. We develop effective and efficient algorithms for various tasks such as content-aware influence maximization and time segmentation. In addition, we study many real-world networks and their summary graphs such as people-contact, news-blogs, etc. and visualize them to make sense of their characteristics given the input task.
2. Learning-based approaches: As our next step, we propose a unified framework which learns the process of summarization itself for a given task. First, we design a generalizable algorithm to learn to summarize graphs for a set of graph optimization problems. Next, we go further and add sparse human feedback to the learning process for the given optimization task.
To the best of our knowledge, we are the first to systematically bring the necessity of considering the given task to the forefront and emphasize the importance of learning-based approaches in network summarization. Our models and frameworks lead to meaningful discoveries. We also solve problems from various domains such as epidemiology, marketing, social media, cybersecurity, and interactive visualization.Doctor of Philosophy
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Amaduzzi, Alberto. "Enzymes' characterization via spectral analysis of the Laplacian associated to their relative contact maps." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23899/.
Full textAbstract:
The main motivation for my thesis is the believe that global properties of enzymes areessential for a complete understanding of their behaviors. In my thesis, in particular,I investigate qualitative properties of enzymes via spectral techniques associated to thegraph Laplacian. I try to apply visualization techniques to understand similarities anddissimilarities among different enzymes’ structures, encoded in adjacency matrices re-trieved from coordinate data in online available datasets. The purpose is to make anexploration of features and see whether these techniques, that are used extensively inliterature for visual discrimination tasks, are also useful for these biological entities.I have tried to design a size-independent analysis that would be able to differentiateamong different taxonomies, different catalytic properties and different environmentsassociated to enzymes. This attempt provided useful hints for the analysis of enzymeproperties, even if as a final remark the dependence from enzyme size is still found inthe Laplacian eigenvalue spectrum.
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Fischer, Manfred M., and Sucharita Gopal. "Spectral Pattern Recognition and Fuzzy ARTMAP Classification: Design Features, System Dynamics and Real World Simulations." WU Vienna University of Economics and Business, 1996. http://epub.wu.ac.at/4163/1/WSG_DP_5296.pdf.
Full textAbstract:
Classification of terrain cover from satellite radar imagery represents an area of considerable
current interest and research. Most satellite sensors used for land applications are of the imaging
type. They record data in a variety of spectral channels and at a variety of ground resolutions.
Spectral pattern recognition refers to classification procedures utilizing pixel-by-pixel spectral
information as the basis for automated land cover classification. A number of methods have
been developed in the past to classify pixels [resolution cells] from multispectral imagery to a
priori given land cover categories. Their ability to provide land cover information with high
classification accuracies is significant for work where accurate and reliable thematic information
is needed. The current trend towards the use of more spectral bands on satellite instruments,
such as visible and infrared imaging spectrometers, and finer pixel and grey level resolutions
will offer more precise possibilities for accurate identification. But as the complexity of the data
grows, so too does the need for more powerful tools to analyse them.
It is the major objective of this study to analyse the capabilities and applicability of the neural
pattern recognition system, called fuzzy ARTMAP, to generate high quality classifications of
urban land cover using remotely sensed images. Fuzzy ARTMAP synthesizes fuzzy logic and
Adaptive Resonance Theory (ART) by exploiting the formal similarity between the
computations of fuzzy subsethood and the dynamics of category choice, search and learning.
The paper describes design features, system dynamics and simulation algorithms of this
learning system, which is trained and tested for classification (8 a priori given classes) of a
multispectral image of a Landsat-5 Thematic Mapper scene (270 x 360 pixels) from the City of
Vienna on a pixel-by-pixel basis. Fuzzy ARTMAP performance is compared with that of an
error-based learning system based upon the multi-layer perceptron, and the Gaussian maximum
likelihood classifier as conventional statistical benchmark on the same database. Both neural
classifiers outperform the conventional classifier in terms of classification accuracy. Fuzzy
ARTMAP leads to out-of-sample classification accuracies, very close to maximum
performance, while the multi-layer perceptron - like the conventional classifier - shows
difficulties to distinguish between some land use categories. (authors' abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScience
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Chowdhury, Arshad M. "Optical Label Switching Technologies for Optical Packet Switched Networks." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14047.
Full textAbstract:
Optical packet switching (OPS) is the most prominent candidate transport solution that can seamlessly integrate electrical and optical layers by transferring certain switching functionality from electronics to optics, thus alleviating unnecessarily slow and expensive optical-electrical-optical conversions and signal processing at the switching node. Optical Label Switching (OLS) is an important aspect of the optical packet switched network that enables very low-latency forwarding of ultra-high bit-rate, protocol-independent packets entirely in the optical domain.
The objective of the proposed research is to develop novel, efficient techniques to realize several key enabling technologies such as optical label generation and encoding, optical label swapping, all-optical buffering, and spectral efficient transmission system for optical label switched based OPS networks. A novel scheme of generating optical label at the ingress node using optical carrier suppression and separation (OCSS) technique is proposed. This scheme does not suffer from any unavoidable interference, limited extinction ratio or strict synchronization requirements between payload and label as observed by the currently available other label generation methods. One of the primary challenges to realize optical label swapping at the core node of scalable OLS network is the insertion of new optical labels without any wavelength conversion for same wavelength packet routing. A novel mechanism to realize same wavelength packet switching without using any conventional wavelength converter in the OLS network carrying differential phase-shift keying (DPSK) modulated payload and on-off keying (OOK) modulated optical label is demonstrated. Also a new dense wavelength division multiplexing (DWDM) optical buffer architecture using optical fiber delay lines that can provide wavelength selective reconfigurable variable delays is proposed. Optical packet switching provides automated, reconfigurable, and faster provision of both wavelength and bandwidth with finer granularity in the optical layer. However, a newer, cost-effective, and spectrally efficient optical transmission technology is essential to support the explosive bandwidth demand expected by the future optical packet switched networks. To meet this challenge, a spectrally efficient solution for transporting 40 Gbps per channel data over 50 GHz spaced DWDM system is developed by exploiting optical carrier suppression and separation technique and optical duobinary modulation.
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Sariaydin, Ayse. "Computation And Analysis Of Spectra Of Large Networks With Directed Graphs." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612249/index.pdf.
Full textAbstract:
Analysis of large networks in biology, science, technology and social systems have become very popular recently. These networks are mathematically represented as graphs. The task is then to extract relevant qualitative information about the empirical networks from the analysis of these graphs.
It was found that a graph can be conveniently represented by the spectrum of a suitable difference operator, the normalized graph Laplacian, which underlies diffusions and random walks on graphs. When applied to large networks, this requires computation of the spectrum of large matrices. The normalized Laplacian matrices representing large networks are usually sparse and unstructured.
The thesis consists in a systematic evaluation of the available eigenvalue solvers for nonsymmetric large normalized Laplacian matrices describing directed graphs of empirical networks. The methods include several Krylov subspace algorithms like implicitly restarted Arnoldi method, Krylov-Schur method and Jacobi-Davidson methods which are freely available as standard packages written in MATLAB or SLEPc, in the library written C++.
The normalized graph Laplacian as employed here is normalized such that its spectrum is confined to the range [0, 2]. The eigenvalue distribution plays an important role in network analysis. The numerical task is then to determine the whole spectrum with appropriate eigenvalue solvers. A comparison of the existing eigenvalue solvers is done with Paley digraphs with known eigenvalues and for citation networks in sizes 400, 1100 and 4500 by computing
the residuals.