Over the last decade, cellular networks have grown rapidly from circuit-switch-based voice-only networks to IP-based data-dominant networks, embracing not only traditional mobile phones, but also smartphones and mobile computers. The ever-increasing demands for reliable and high-speed data services have challenged the capacity and coverage of cellular networks. Research and development on femtocells seeks to provide a solution to fill coverage holes and to increase the network capacity to accommodate more mobile terminals and applications that requires higher bandwidth. Among the challenges associated with introducing femtocells in existing cellular networks, interference management and resource allocation are critical. In this dissertation, we address fundamental aspects of resource allocation for cellular networks with coexisting femtocells and macrocells on the downlink side, addressing questions such as: How many additional resource blocks are required to add femtocells into the current cellular system? What is the best way to reuse resources between femtocells and macrocells? How can we efficiently assign limited resources to network users? In this dissertation, we develop an analytical model of resource allocation based on random graphs. In this model, arbitrarily chosen communication links interfere with each other with a certain probability. Using this model, we establish asymptotic bounds on the minimum number of resource blocks required to make interference-free resource assignments for all the users in the network. We assess these bounds using a simple greedy resource allocation algorithm to demonstrate that the bounds are reasonable in finite networks of plausible size. By applying the bounds, we establish the expected impact of femtocell networks on macrocell resource allocation under a variety of interference scenarios. We proceed to compare two reuse schemes, termed shared reuse and split reuse, using three social welfare functions, denoted utilitarian fitness, egalitarian fitness, and proportionally fair fitness. The optimal resource split points, which separate resource access by femtocells and macrocells, are derived with respect to the above fitness functions. A set of simple greedy resource allocation algorithms are developed to verify our analysis and compare fitness values of the two reuse schemes under various network scenarios. We use the obtained results to assess the efficiency loss associated with split reuse, as an aid to determining whether resource allocators should use the simpler split reuse scheme or attempt to tackle the complexity and overhead associated with shared reuse. Due to the complexity of the proportionally fair fitness function, optimal resource allocation for cellular networks with femtocells and macrocells is difficult to obtain. We develop a genetic algorithm-based centralized resource allocation algorithm to yield suboptimal solutions for such a problem. The results from the genetic algorithm are used to further assess the performance loss of split reuse and provide a baseline suboptimal resource allocation. Two distributed algorithms are then proposed to give a practical solution to the resource allocation problem. One algorithm is designed for a case with no communications between base stations and another is designed to exploit the sharing of information between base stations. The numerical results from these distributed algorithms are then compared against to the ones obtained by the genetic algorithm and the performance is found to be satisfactory, typically falling within 8\% of the optimum social welfare found via the genetic algorithm. The capability of the distributed algorithms in adapting to network changes is also assessed and the results are promising. All of the work described thus far is carried out under a protocol model in which interference between two links is a binary condition. Though this model makes the problem more analytically tractable, it lacks the ability to reflect additive interference as in the SINR model. Thus, in the final part of our work, we apply conflict-free resource allocations from our distributed algorithms to simulated networks and examine the allocations under the SINR model to evaluate feasibility. This evaluation study confirms that the protocol-model-based algorithms, with a small adjustment, offer reasonable performance even under the more realistic SINR model. This work was supported by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice under Award No. 2005-IJ-CX-K017 and the National Science Foundation under Grant No. 0448131. Any opinions, findings, and conclusions or recommendations expressed in this dissertation are those of the author and do not necessarily reflect the views of the National Institute of Justice or the National Science Foundation. The NSF/TEKES Wireless Research Exchange Program also contributed to this work by funding a summer study. / Ph. D.

Emerging wireless networks are foreseen as an integration of heterogeneous spectrum bands, wireless access technologies, and backhaul solutions, as well as a large-scale interconnection of devices, people, and vehicles. Such a heterogeneity will range from the proliferation of multi-tasking user devices with different capabilities such as smartphones and tablets to the deployment of multi-mode access points that can operate over heterogeneous frequency bands spanning both sub-6 GHz microwave and high-frequency millimeter wave (mmW) frequencies bands. This heterogeneous ecosystem will yield new challenges and opportunities for wireless resource management. On the one hand, resource management can exploit user and network-specific context information, such as application type, social metrics, or operator pricing, to develop application-driven, context-aware networks. Similarly, multiple frequency bands can be leveraged to meet the stringent and heterogeneous quality-of-service (QoS) requirements of the new wireless services such as video streaming and interactive gaming. On the other hand, resource management in such heterogeneous, multi-band, and large-scale wireless systems requires distributed frameworks that can effectively utilize all available resources while operating with manageable overhead. The key goal of this dissertation is therefore to develop novel, self-organizing, and low-complexity resource management protocols -- using techniques from matching theory, optimization, and machine learning -- to address critical resource allocation problems for emerging heterogeneous wireless systems while explicitly modeling and factoring diverse network context information. Towards achieving this goal, this dissertation makes a number of key contributions. First, a novel context-aware scheduling framework is developed for enabling dual-mode base stations to efficiently and jointly utilize mmW and microwave frequency resources while maximizing the number of user applications whose stringent delay requirements are satisfied. The results show that the proposed approach will be able to significantly improve the QoS per application and decrease the outage probability. Second, novel solutions are proposed to address both network formation and resource allocation problems in multi-hop wireless backhaul networks that operate at mmW frequencies. The proposed framework motivates collaboration among multiple network operators by resource sharing to reduce the cost of backhauling, while jointly accounting for both wireless channel characteristics and economic factors. Third, a novel framework is proposed to exploit high-capacity mmW communications and device-level caching to minimize handover failures as well as energy consumption by inter-frequency measurements, and to provide seamless mobility in dense heterogeneous mmW-microwave small cell networks (SCNs). Fourth, a new cell association algorithm is proposed, based on matching theory with minimum quota constraints, to optimize load balancing in integrated mmW-microwave networks. Fifth, a novel medium access control (MAC) protocol is proposed to dynamically manage the wireless local area network (WLAN) traffic jointly over the unlicensed 60 GHz mmW and sub-6 GHz bands to maximize the saturation throughput and minimize the delay experienced by users. Finally, a novel resource management approach is proposed to optimize device-to-device (D2D) communications and improve traffic offload in heterogeneous wireless SCNs by leveraging social context information that is dynamically learned by the network. In a nutshell, by providing novel, context-aware, and self-organizing frameworks, this dissertation addresses fundamentally challenging resource management problems that mainly stem from large scale, stringent service requirements, and heterogeneity of next-generation wireless networks. / Ph. D. / The emergence of bandwidth-intensive applications along with vast proliferation of smart, multi-tasking handhelds have strained the capacity of wireless networks. Furthermore, the landscape of wireless communications is shifting towards providing connectivity, not only to humans, but also to automated cars, drones, and robots, among other critical applications. These new technologies will enable devices, machines, and things to be more intuitive, while being more capable, in order to improve the quality of life for human. For example, in future networked life, smartphones will predict our needs and help us with providing timely and relevant information from our surrounding. As an another example, autonomous vehicles and smart transportation systems with large number of connected safety features will minimize road incidents and yield a safe and joyful driving experience. Turning such emerging services into reality will require new technology innovations that provide high efficiency and substantial levels of scalability. To this end, wireless communication is the key candidate to provide large-scale and ubiquitous connectivity. However, existing wireless networks operate at congested microwave (µW) frequency bands and cannot manage the exponential growth in wireless data traffic or support low latency and ultra-high reliability communications, required by many emerging critical applications. Therefore, the goal of this dissertation is to develop novel network resource utilization frameworks to efficiently manage the heterogeneous traffic in next-generation wireless networks, while meeting their stringent quality-of-service (QoS) requirements. This transformative, fundamental research will expedite the deployment of communications at very high frequencies, at the millimeter wave (mmW) frequency bands, in next-generation wireless networks. The developed frameworks will advance new concepts from matching theory and machine learning for resource management in cellular networks, wireless local area networks (WLANs), and the intersection of these systems at both mmW and µW unlicensed frequency bands. This multi-band networking will leverage the synergies between mmW and µW wireless networks to provide robust and cost-effective solutions that enable the support of heterogeneous traffic from future wireless services. The anticipated results will transform the way in which spectral and time resources are used in both cellular networks and WLANs.

Yes / This study aims to introduce a new phased array design with improved radiation properties for future cellular networks. The procedure of the array design is simple and has been accomplished on a low-cost substrate material while offering several interesting features with high performance. Its schematic involves eight air-filled slot-loop metal-ring elements with a 1 × 8 linear arrangement at the top edge of the 5G smartphone mainboard. Considering the entire board area, the proposed antenna elements occupy an extremely small area. The antenna elements cover the range of 21–23.5 GHz sub-mm-wave 5G bands. Due to the air-filled function in the configurations of the elements, low-loss and high-performance radiation properties are observed. In addition, the fundamental characteristics of the introduced array are insensitive to various types of substrates. Moreover, its radiation properties have been compared with conventional arrays and better results have been observed. The proposed array appears with a simple design, a low complexity profile, and its attractive broad impedance bandwidth, end-fire radiation mode, wide beam steering, high radiation coverage, and stable characteristics meet the needs of 5G applications in future cellular communications. Additionally, the smartphone array design offers sufficient efficiency when it comes to the appearance and integration of the user’s components. Thus, it could be used in 5G hand-portable devices.

The distributed and privacy sensitive nature of cellular networks make them strong candidates for optimization using Federated Learning, but this exposes them to a problem inherent to the learning paradigm: performance inequality due to heterogeneous client data distributions. The prevailing approach of enforcing uniform client performance ignores client-specific performance limitations due to different levels of irreducible uncertainty present in their data, resulting in deteriorated network performance. To address this issue, this thesis introduces two novel federated algorithms designed to enhance learning efficiency and ensure fairness in the presence of heteroscedastic noise, reflecting the distributive justice principles of utilitarianism and equality. Under these circumstances, the proposed algorithms are shown to significantly improve overall performance and performance fairness. The deployment of these algorithms promises a dual benefit: enhancement in network performance and a fairer distribution of service quality for end users.

Over recent years, wireless communication systems have been experiencing a dramatic and continuous growth in the number of subscribers, thus placing extra demands on system capacity. At the same time, keeping Quality of Service (QoS) at an acceptable level is a critical concern and a challenge to the wireless network designer. In this sense, performance analysis must be the first step in designing or improving a network. Thus, powerful mathematical tools for analysing most of the performance metrics in the network are required. A good modelling and analysis of the wireless cellular networks will lead to a high level of QoS. In this thesis, different analytical models of various handoff schemes and resource re-allocation in homogeneous and heterogeneous wireless cellular networks are developed and investigated. The sustained increase in users and the request for advanced services are some of the key motivations for considering the designing of Hierarchical Cellular Networks (HCN). In this type of system, calls can be blocked in a microcell flow over to an overlay macrocell. Microcells in the HCN can be replaced by WLANs as this can provide high bandwidth and its users have limited mobility features. Efficient sharing of resources between wireless cellular networks and WLANs will improve the capacity as well as QoS metrics. This thesis first presents an analytical model for priority handoff mechanisms, where new calls and handoff calls are captured by two different traffic arrival processes, respectively. Using this analytical model, the optimised number of channels assigned to II handover calls, with the aim of minimising the drop probability under given network scenarios, has been investigated. Also, an analytical model of a network containing two cells has been developed to measure the different performance parameters for each of the cells in the network, as well as altogether as one network system. Secondly, a new solution is proposed to manage the bandwidth and re-allocate it in a proper way to maintain the QoS for all types of calls. Thirdly, performance models for microcells and macrocells in hierarchical cellular networks have been developed by using a combination of different handoff schemes. Finally, the microcell in HCN is replaced by WLANs and a prioritised vertical handoff scheme in an integrated UMTS/WLAN network has been developed. Simulation experiments have been conducted to validate the accuracy of these analytical models. The models have then been used to investigate the performance of the networks under different scenarios.

En cancérologie, les puces à ADN mesurant le transcriptome sont devenues un outil commun pour chercher à caractériser plus finement les pathologies, dans l’espoir de trouver au travers des expressions géniques : des mécanismes,des classes, des associations entre molécules, des réseaux d’interactions cellulaires. Ces réseaux d’interactions sont très intéressants d’un point de vue biologique car ils concentrent un grand nombre de connaissances sur le fonctionnement cellulaire. Ce travail de thèse a pour but, à partir de ces mêmes données d’expression, d’extraire des structures pouvant s’apparenter à des réseaux d’interactions génétiques. Le cadre méthodologique choisi pour appréhender cette problématique est les « Réseaux Bayésiens », c’est-à-dire une méthode à la fois graphique et probabiliste permettant de modéliser des systèmes pourtant statiques (ici le réseau d’expression génétique) à l’aide d’indépendances conditionnelles sous forme d’un réseau. L’adaptation de cette méthode à des données dont la dimension des variables (ici l’expression des gènes, dont l’ordre de grandeur est 105) est très supérieure à la dimension des échantillons (ordre102 en cancérologie) pose des problèmes statistiques (de faux positifs et négatifs) et combinatoires (avec seulement 10gènes on a 4×1018 graphes orientés sans circuit possibles). A partir de plusieurs problématiques de cancers (leucémies et cancers du sein), ce projet propose une stratégie d’accélération de recherche de réseaux d’expression à l’aide de Réseaux Bayésiens, ainsi que des mises en œuvre de cette méthode pour classer des tumeurs, sélectionner un ensemble de gènes d’intérêt reliés à une condition biologique particulière, rechercher des réseaux locaux autour d’un gène d’intérêt.On propose parallèlement de modéliser un Réseau Bayésien à partir d’un réseau biologique connu, utile pour simuler des échantillons et tester des méthodes de reconstruction de graphes à partir de données contrôlées. / In oncology, microarrays have become a classical tool to search and characterize pathologies at a deeper level than previous methods, using genetic expression to find the mechanisms, classes, molecular associations, and cellular interaction networks of different cancers. From a biological point of view, these cellular networks are interesting because they concentrate a large amount of knowledge about cellular processes. The goal of this PhD thesis project is to extract structures that could correspond to genetic interaction networks from the expression data. "Bayesian Networks", i.e. a graphic and probabilistic method that models even static systems (like the expression network) with conditional independences, are used as the framework to investigate this problem. The adaptation of this method to data where the dimension of the variables (about 105 for gene expression) is much greater than the dimension of the samples (about 102 in oncology) aggravates some statistical and combinatorial problems. For several cancer problematics, this project proposes an acceleration strategy for capturing expression networks with Bayesian Networks and some methods to classify tumors, finding gene signatures of particular biological conditions by searching for local networks in the neighborhood of a gene of interest. In parallel, we propose to model a Bayesian Network from a known biological network, which is useful to simulate samples and to test these methods to reconstruct graphs from

The mobile traffic in cellular networks is increasing in a steady rate as we go intoa future where we are connected to the internet practically all the time in one wayor another. To map the mobile traffic and the volume pressure on the base stationduring different time periods, it is useful to have the ability to predict the trafficvolumes within cellular networks. The data in this work consists of 4G cellular trafficdata spanning over a 7 day coherent period, collected from cells in a moderately largecity. The proposed method in this work is ARIMA modeling, in both original formand with an extension where the coefficients of the ARIMA model are re-esimated byintroducing some user characteristic variables. The re-estimated coefficients produceslightly lower forecast errors in general than a isolated ARIMA model where thevolume forecasts only depends on time. This implies that the forecasts can besomewhat improved when we allow the influence of these variables to be a part ofthe model, and not only the time series itself.

A new class of wireless sensor networks has recently appeared due to the pervasiness of cellular phones with embedded sensors, mobile Internet connectivity, and location technologies. This mobile wireless sensor network has the potential to address large-scale societal problems and improve the people's quality of life in a better, faster and less expensive fashion than current solutions based on static wireless sensor networks. Ubiquitous Sensing is the umbrella term used in this dissertation that encompasses location-based services, human-centric, and participatory sensing applications. At the same time, ubiquitous sensing applications are bringing a new series of challenging problems. This dissertation proposes and evaluates G-Sense, for Global-Sense, an architecture that integrates mobile and static wireless sensor networks, and addresses several new problems related to location-based services, participatory sensing, and human-centric sensing applications. G-Sense features the critical point algorithms, which are specific mechanisms to reduce the power consumption by continous sensing applications in cellular phones, and reduce the amount of data generated by these applications. As ubiquitous sensing applications have the potential to gather data from many users around the globe, G-Sense introduces a peer-to-peer system to interconnect sensing servers based on the locality of the data. Finally, this dissertation proposes and evaluates a multiobjective model and a hybrid evolutionary algorithm to address the efficient deployment of static wireless sensor nodes when monitoring critical areas of interest.

An ever increasing demand for mobile broadband applications and services is leading to a massive network densification. The current cellular system architectures are both economically and ecologically limited to handle this. The concept of small-cell networks (SCNs) based on the idea of dense deployment of self-organizing; low-cost, low-power base station (BSs) is a promising alternative. Although SCNs have the potential to significantly increase the capacity and coverage of cellular networks while reducing their energy consumption, they pose many new challenges to the optimal system design. Due to small cell sizes, the mobile users cross over many cells during the course of their service resulting in frequent handovers. Also, due to proximity of BSs, users (especially those at cell edges) experience a higher degree of interference from neighboring BSs. If one has to derive advantages from SCNs, these alleviated effects have to be taken care either by compromising on some aspects of optimality (like dedicating extra resources) or by innovating smarter algorithms or by a combination of the two. The concept of umbrella cells is introduced to take care of frequent handovers. Here extra resources are dedicated to ensure that the calls are not dropped within an umbrella cell. To manage interference, one might have to ensure that the neighboring cells always operate in independent channels or design algorithms which work well in interference dominant scenarios or use the backhaul to incorporate BS cooperation techniques. Further, small cell BS are most often battery operated, which calls for efficient power utilization and energy conservation techniques. Also, when deployed in urban areas, some of the small cells can have larger concentration of users throughout the cell, for example, hot-spots, which call in for design of SCNs with dense users. Also, with portable BSs, one has the choice to install them on street infrastructure or within residential complexes. In such cases, cell design and resource allocation has to consider aspects like user density, distribution (indoor/outdoor), mobility, attenuation, etc. We present the thesis in two parts. In the first part we study the cell design aspects, while the second part deals with the resource allocation. While the focus is on SCNs, some of the results derived and the tools and techniques used are also applicable to conventional cellular systems.

In a multihop cellular network, mobile terminals have the capability to transmit directly to other mobile terminals enabling them to use other terminals as relays to forward traffic towards the base station. Previous studies of networks consisting of a single cell found that the SINR in a multihop cellular network is slightly lower than in a traditional cellular network. However, multihop cellular networks may have a higher capacity than traditional cellular networks due to their potential for lower intercell interference. For this reason, the effects of intercell interference are investigated in this thesis. Our simulations of a network with many cells show that multihop cellular networks have a higher SINR than traditional cellular networks due to the near elimination of intercell interference. However, multihop cellular networks still suffer from large amounts of interference surrounding the base station because all traffic either emanates or is destined to the base station making it the capacity bottleneck. To resolve this problem, we propose a novel architecture called the autonomous infrastructure multihop cellular network where users can connect their mobile terminals to the backbone network giving them the functionality of an access point. Access points receive traffic from other terminals and send it directly onto the backbone, as would a base station. This reduces the traffic handled by the base station and increases network capacity. Our analysis and simulations show that in autonomous infrastructure multihop cellular networks, the SINR at the base station is higher, the power consumption is lower and the coverage is better than in normal multihop cellular networks. Access points require parameters like their transmission range to be adjusted autonomously to optimal levels. In this thesis, we propose an autonomous pilot power protocol. Our results show that by adjusting a parameter within the protocol, a required coverage level of terminals can be specified and achieved without knowledge of the location or density of mobile terminals. Furthermore, we show that the protocol determines the transmission range that is optimal in terms of SINR and power consumption that achieves the required coverage while effectively eliminating the bottleneck that existed at the base station.