La capacité à prévoir les activités humaines a des implications essentielles dans de nombreux aspects des réseaux cellulaires. En particulier, la haute disponibilité de la prédiction de la mobilité peut permettre différents scénarios d'application; une meilleure compréhension de la demande de trafic de données mobiles peut aider à améliorer la conception de solutions pour l'équilibrage de la charge du réseau. Bien que de nombreux chercheurs aient étudié le sujet de la prédiction de la mobilité humaine, il y a eu peu de discussions sur l'anticipation du trafic de données mobiles dans les réseaux cellulaires.Pour comprendre la mobilité humaine, les ensembles de données de téléphones mobiles, consistant en des enregistrements de données de taxation (CDR), constituent un choix pratique d'empreintes humaines. Comme le déploiement du réseau cellulaire est très irrégulier et que les fréquences d'interaction sont généralement faibles, les données CDR sont souvent caractérisées par une parcimonie spatio-temporelle qui, à son tour, peut biaiser les analyses de mobilité basées sur de telles données et provoquer la perte de trajectoires individuelles.Dans cette thèse, nous présentons de nouvelles solutions de reconstruction de trajectoires individuelles et de prédiction de trafic de données mobiles individuelles. Nos contributions abordent les problèmes de (1) surmonter l'incomplétude des informations de mobilité pour l'utilisation des ensembles de données de téléphonie mobile et (2) prédire la future demande de trafic de données mobiles pour le support des applications de gestion de réseau.Premièrement, nous nous concentrons sur la faille de l'information sur la mobilité dans les ensembles de données de téléphones mobiles. Nous rapportons une analyse en profondeur de son effet sur la mesure des caractéristiques de mobilité individuelles et l'exhaustivité des trajectoires individuelles. En particulier, (1) nous fournissons une confirmation des résultats antérieurs concernant les biais dans les mesures de mobilité causées par la rareté temporelle de la CDR; (2) nous évaluons le décalage géographique provoqué par la cartographie des emplacements des utilisateurs vers les tours cellulaires et révélons le biais causé par la rareté spatiale de la CDR; (3) nous fournissons une estimation empirique de l'exhaustivité des données des trajectoires CDR individuelles. (4) nous proposons de nouvelles solutions de complétion CDR pour reconstruire incomplète. Nos solutions tirent parti de la nature des modèles de mouvements humains répétitifs et des techniques d'inférence de données de pointe et surpassent les approches précédentes illustrées par des simulations axées sur les données.Deuxièmement, nous abordons la prédiction des demandes de trafic de données mobiles générées par les abonnés individuels du réseau mobile. Sur la base de trajectoires complétées par nos solutions développées et nos historiques de consommation de données extraites d'un ensemble de données de téléphonie mobile à grande échelle, (1) nous étudions les limites de prévisibilité en mesurant la prévisibilité maximale que tout algorithme peut atteindre. les approches de prédiction du trafic de données mobiles qui utilisent les résultats de l'analyse théorique de la prévisibilité. Notre analyse théorique montre qu'il est théoriquement possible d'anticiper la demande individuelle avec une précision typique de 75% malgré l'hétérogénéité des utilisateurs et avec une précision améliorée de 80% en utilisant la prédiction conjointe avec des informations de mobilité. Notre pratique basée sur des techniques d'apprentissage automatique peut atteindre une précision typique de 65% et avoir un degré d'amélioration de 1% à 5% en considérant les déplacements individuels.En résumé, les contributions mentionnées ci-dessus vont dans le sens de l'utilisation des ensembles de données de téléphonie mobile et de la gestion des opérateurs de réseau et de leurs abonnés. / The understanding of human behaviors is a central question in multi-disciplinary research and has contributed to a wide range of applications. The ability to foresee human activities has essential implications in many aspects of cellular networks. In particular, the high availability of mobility prediction can enable various application scenarios such as location-based recommendation, home automation, and location-related data dissemination; the better understanding of mobile data traffic demand can help to improve the design of solutions for network load balancing, aiming at improving the quality of Internet-based mobile services. Although a large and growing body of literature has investigated the topic of predicting human mobility, there has been little discussion in anticipating mobile data traffic in cellular networks, especially in spatiotemporal view of individuals.For understanding human mobility, mobile phone datasets, consisting of Charging Data Records (CDRs), are a practical choice of human footprints because of the large-scale user populations and the vast diversity of individual movement patterns. The accuracy of mobility information granted by CDR depends on the network infrastructure and the frequency of user communication events. As cellular network deployment is highly irregular and interaction frequencies are typically low, CDR data is often characterized by spatial and temporal sparsity, which, in turn, can bias mobility analyses based on such data and cause the loss of whereabouts in individual trajectories.In this thesis, we present novel solutions of the reconstruction of individual trajectories and the prediction of individual mobile data traffic. Our contributions address the problems of (1) overcoming the incompleteness of mobility information for the use of mobile phone datasets and (2) predicting future mobile data traffic demand for the support of network management applications.First, we focus on the flaw of mobility information in mobile phone datasets. We report on an in-depth analysis of its effect on the measurement of individual mobility features and the completeness of individual trajectories. In particular, (1) we provide a confirmation of previous findings regarding the biases in mobility measurements caused by the temporal sparsity of CDR; (2) we evaluate the geographical shift caused by the mapping of user locations to cell towers and reveal the bias caused by the spatial sparsity of CDR; (3) we provide an empirical estimation of the data completeness of individual CDR-based trajectories. (4) we propose novel solutions of CDR completion to reconstruct incomplete. Our solutions leverage the nature of repetitive human movement patterns and the state-of-the-art data inference techniques and outperform previous approaches shown by data-driven simulations.Second, we address the prediction of mobile data traffic demands generated by individual mobile network subscribers. Building on trajectories completed by our developed solutions and data consumption histories extracted from a large-scale mobile phone dataset, (1) we investigate the limits of predictability by measuring the maximum predictability that any algorithm has potential to achieve and (2) we propose practical mobile data traffic prediction approaches that utilize the findings of the theoretical predictability analysis. Our theoretical analysis shows that it is theoretically possible to anticipate the individual demand with a typical accuracy of 75% despite the heterogeneity of users and with an improved accuracy of 80% using joint prediction with mobility information. Our practical based on machine learning techniques can achieve a typical accuracy of 65% and have a 1%~5% degree of improvement by considering individual whereabouts.In summary, the contributions mentioned above provide a step further towards supporting the use of mobile phone datasets and the management of network operators and their subscribers.

Antenna tilt is the angle subtended by the radiation beam and horizontal plane. This angle plays a vital role in determining the coverage and the interference of the network with neighbouring cells and adjacent base stations. Traditional methods for network optimization rely on rule-based heuristics to do decision making for antenna tilt optimization to achieve desired network characteristics. However, these methods are quite brittle and are incapable of capturing the dynamics of communication traffic. Recent advancements in reinforcement learning have made it a viable solution to overcome this problem but even this learning approach is either limited to its simulation environment or is limited to off-policy offline learning. So far, there has not been any effort to overcome the previously mentioned limitations, so as to make it applicable in the real world. This work proposes a method that consists of transferring reinforcement learning policies from a simulated environment to a real environment i.e. sim-to-real transfer through the use of offline learning. The approach makes use of a simulated environment and a fixed dataset to compensate for the underlined limitations. The proposed sim-to-real transfer technique utilizes a hybrid policy model, which is composed of a portion trained in simulation and a portion trained on the offline real-world data from the cellular networks. This enables to merge samples from the real-world data to the simulated environment consequently modifying the standard reinforcement learning training procedures through knowledge sharing between the two environment’s representations. On the one hand, simulation enables to achieve better generalization performance with respect to conventional offline learning as it complements offline learning with learning through unseen simulated trajectories. On the other hand, the offline learning procedure enables to close the sim-to-real gap by exposing the agent to real-world data samples. Consequently, this transfer learning regime enable us to establish optimal antenna tilt control which in turn results in improved coverage and reduced interference with neighbouring cells in the cellular network. / Antennlutning är den vinkel som dämpas av strålningsstrålen och det horisontella planet. Denna vinkel spelar en viktig roll för att bestämma täckningen och störningen av nätverket med angränsande celler och intilliggande basstationer. Traditionella metoder för nätverksoptimering förlitar sig på regelbaserad heuristik för att göra beslutsfattande för antennlutningsoptimering för att uppnå önskade nätverksegenskaper. Dessa metoder är dock ganska styva och är oförmögna att fånga dynamiken i kommunikationstrafiken. De senaste framstegen inom förstärkningsinlärning har gjort det till en lönsam lösning att lösa detta problem, men även denna inlärningsmetod är antingen begränsad till dess simuleringsmiljö eller är begränsad till off-policy offline inlärning. Hittills har inga ansträngningar gjorts för att övervinna de tidigare nämnda begränsningarna för att göra det tillämpligt i den verkliga världen. Detta arbete föreslår en metod som består i att överföra förstärkningsinlärningspolicyer från en simulerad miljö till en verklig miljö, dvs. sim-till-verklig överföring genom användning av offline-lärande. Metoden använder en simulerad miljö och en fast dataset för att kompensera för de understrukna begränsningarna. Den föreslagna sim-till-verkliga överföringstekniken använder en hybridpolicymodell, som består av en del utbildad i simulering och en del utbildad på offline-verkliga data från mobilnätverk. Detta gör det möjligt att slå samman prover från verklig data till den simulerade miljön och därmed modifiera standardutbildningsförfarandena för förstärkning genom kunskapsdelning mellan de två miljöernas representationer. Å ena sidan möjliggör simulering att uppnå bättre generaliseringsprestanda med avseende på konventionellt offlineinlärning eftersom det kompletterar offlineinlärning med inlärning genom osynliga simulerade banor. Å andra sidan möjliggör offline-inlärningsförfarandet att stänga sim-till-real-klyftan genom att exponera agenten för verkliga dataprov. Följaktligen möjliggör detta överföringsinlärningsregime att upprätta optimal antennlutningskontroll som i sin tur resulterar i förbättrad täckning och minskad störning med angränsande celler i mobilnätet.

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L’émergence de nouvelles applications et de nouveaux services (tels que les applications multimédias, la voix-sur-IP, la télévision-sur-IP, la vidéo-sur-demande, etc.) et le besoin croissant de mobilité des utilisateurs entrainent une demande de bande passante de plus en plus croissante et une difficulté dans sa gestion dans les réseaux cellulaires sans fil (WCNs), causant une dégradation de la qualité de service. Ainsi, dans cette thèse, nous nous intéressons à la gestion des ressources, plus précisément à la bande passante, dans les WCNs. Dans une première partie de la thèse, nous nous concentrons sur la prédiction de la mobilité des utilisateurs des WCNs. Dans ce contexte, nous proposons un modèle de prédiction de la mobilité, relativement précis qui permet de prédire la destination finale ou intermédiaire et, par la suite, les chemins des utilisateurs mobiles vers leur destination prédite. Ce modèle se base sur : (a) les habitudes de l’utilisateur en terme de déplacements (filtrées selon le type de jour et le moment de la journée) ; (b) le déplacement courant de l’utilisateur ; (c) la connaissance de l’utilisateur ; (d) la direction vers une destination estimée ; et (e) la structure spatiale de la zone de déplacement. Les résultats de simulation montrent que ce modèle donne une précision largement meilleure aux approches existantes. Dans la deuxième partie de cette thèse, nous nous intéressons au contrôle d’admission et à la gestion de la bande passante dans les WCNs. En effet, nous proposons une approche de gestion de la bande passante comprenant : (1) une approche d’estimation du temps de transfert intercellulaire prenant en compte la densité de la zone de déplacement en terme d’utilisateurs, les caractéristiques de mobilité des utilisateurs et les feux tricolores ; (2) une approche d’estimation de la bande passante disponible à l’avance dans les cellules prenant en compte les exigences en bande passante et la durée de vie des sessions en cours ; et (3) une approche de réservation passive de bande passante dans les cellules qui seront visitées pour les sessions en cours et de contrôle d’admission des demandes de nouvelles sessions prenant en compte la mobilité des utilisateurs et le comportement des cellules. Les résultats de simulation indiquent que cette approche réduit largement les ruptures abruptes de sessions en cours, offre un taux de refus de nouvelles demandes de connexion acceptable et un taux élevé d’utilisation de la bande passante. Dans la troisième partie de la thèse, nous nous penchons sur la principale limite de la première et deuxième parties de la thèse, à savoir l’évolutivité (selon le nombre d’utilisateurs) et proposons une plateforme qui intègre des modèles de prédiction de mobilité avec des modèles de prédiction de la bande passante disponible. En effet, dans les deux parties précédentes de la thèse, les prédictions de la mobilité sont effectuées pour chaque utilisateur. Ainsi, pour rendre notre proposition de plateforme évolutive, nous proposons des modèles de prédiction de mobilité par groupe d’utilisateurs en nous basant sur : (a) les profils des utilisateurs (c’est-à-dire leur préférence en termes de caractéristiques de route) ; (b) l’état du trafic routier et le comportement des utilisateurs ; et (c) la structure spatiale de la zone de déplacement. Les résultats de simulation montrent que la plateforme proposée améliore la performance du réseau comparée aux plateformes existantes qui proposent des modèles de prédiction de la mobilité par groupe d’utilisateurs pour la réservation de bande passante. / The emergence of new applications and services (e.g., multimedia applications, voice over IP and IPTV) and the growing need for mobility of users cause more and more growth of bandwidth demand and a difficulty of its management in Wireless Cellular Networks (WCNs). In this thesis, we are interested in resources management, specifically the bandwidth, in WCNs. In the first part of the thesis, we study the user mobility prediction that is one of key to guarantee efficient management of available bandwidth. In this context, we propose a relatively accurate mobility prediction model that allows predicting final or intermediate destinations and subsequently mobility paths of mobile users to reach these predicted destinations. This model takes into account (a) user’s habits in terms of movements (filtered according to the type of day and the time of the day); (b) user's current movement; (c) user’s contextual knowledge; (d) direction from current location to estimated destination; and (e) spatial conceptual maps. Simulation results show that the proposed model provides good accuracy compared to existing models in the literature. In the second part of the thesis, we focus on call admission control and bandwidth management in WCNs. Indeed, we propose an efficient bandwidth utilization scheme that consists of three schemes: (1) handoff time estimation scheme that considers navigation zone density in term of users, users’ mobility characteristics and traffic light scheduling; (2) available bandwidth estimation scheme that estimates bandwidth available in the cells that considers required bandwidth and lifetime of ongoing sessions; and (3) passive bandwidth reservation scheme that passively reserves bandwidth in cells expected to be visited by ongoing sessions and call admission control scheme for new call requests that considers the behavior of an individual user and the behavior of cells. Simulation results show that the proposed scheme reduces considerably the handoff call dropping rate while maintaining acceptable new call blocking rate and provides high bandwidth utilization rate. In the third part of the thesis, we focus on the main limitation of the first and second part of the thesis which is the scalability (with the number of users) and propose a framework, together with schemes, that integrates mobility prediction models with bandwidth availability prediction models. Indeed, in the two first contributions of the thesis, mobility prediction schemes process individual user requests. Thus, to make the proposed framework scalable, we propose group-based mobility prediction schemes that predict mobility for a group of users (not only for a single user) based on users’ profiles (i.e., their preference in terms of road characteristics), state of road traffic and users behaviors on roads and spatial conceptual maps. Simulation results show that the proposed framework improves the network performance compared to existing schemes which propose aggregate mobility prediction bandwidth reservation models.

O crescente aumento da demanda de tráfego nas redes celulares vem aumentando a necessidade de uma melhor utilização dos recursos do sistema, já que sua expansão é custosa. Nas estações rádio base (ERB), a disponibilidade de largura de faixa de freqüências é limitada e desta maneira, em uma rede de comunicação móvel celular, o controle de admissão de chamadas exerce grande influência no desempenho do sistema, pois determina a utilização de banda das ERBs e se uma determinada quantidade de recursos (banda) será alocado ou não para uma determinada chamada. O desempenho da rede pode ser atrelado a determinados parâmetros, como a probabilidade de bloqueio de novas chamadas, probabilidade de bloqueio de chamadas handoff e a utilização de banda da rede. Este trabalho propõe um controle de admissão de chamadas que, no atendimento de uma chamada, faz o empréstimo de banda de chamadas em andamento na célula no caso de banda insuficiente. O sistema adota um mecanismo heurístico que determina a banda disponível para novas chamadas conforme os valores de certos parâmetros do sistema. O empréstimo de banda é realizado em chamadas em andamento nas células até níveis mínimos estabelecidos para cada tipo de chamada, que se diferenciam pelas necessidades de banda de cada uma. O algoritmo foi aplicado às bandas e características de uma rede de terceira geração (3G), que possui chamadas de voz, videoconferência, interação multimídia, e-mail, downloads e transferência de arquivos e a uma rede GSM/GPRS (global system for mobile communications/ general packet radio service), que possui chamadas de voz e de dados. Os resultados mostram melhorias na probabilidade de bloqueio de novas chamadas, probabilidade de bloqueio de handoff e na utilização de banda do sistema. / The recent growth in traffic loads in cellular networks has seen the need for a better use of system resources as its expansion is expensive. In the base transceiver station (BTS), the bandwidth availability is limited. Thus, in cellular networks the call admission control greatly influences the system performance because it determines the bandwidth use of the BTSs and if an amount of resources will or will not be allocated to a call. The network performance can be evaluated by parameters such as blocking probability of new calls, dropping probability of handoff calls and bandwidth use. This work proposes a call admission control that carries out the bandwidth borrowing when a call arrives and there is not enough bandwidth. The system makes use of a heuristic mechanism that determines the available bandwidth for the new calls according to some parameter values of the system. The bandwidth borrowing is applied to the cell ongoing calls until the minimum levels for each type are met. The algorithm was applied to the bandwidths and characteristics of a third generation cellular network, which supports voice calls, videoconference, multimedia interaction, e-mails, downloads and file transfers. It was also applied to a GSM/GPRS (global system for mobile communications/ general packet radio service), which supports voice and data calls. The results show improvements in the blocking probability of new calls, dropping probability of handoff calls and in the bandwidth use of the system.

In this thesis we address the problem of providing end-to-end quality of service (QoS) to real-time and data connections in a third generation (3G) cellular network based on the Universal Mobile Telecommunication System (UMTS) standard. Data applications usually use TCP (Transmission Control Protocol) and the QoS is a minimum guaranteed mean throughput. For this one first needs to compute the throughput of a TCP connection sending its traffic through the UMTS network (possibly also through the wired part of the Internet). Thus we obtain closed form expressions for a TCP throughput in a UMTS environment. For downloading data at a mobile terminal, the packets of each TCP connection are stored in separate queues at the base station (node B). These are fragmented into Protocol Data Units (PDU). The link layer uses ARQ (Automatic Repeat Request). Thus there can be significant random transmission/queueing delays of TCP packets at the node B. On the other hand the link may not be fully utilized due to the delays of the TCP packets in the rest of the network. In such a scenario the existing models of TCP may not be sufficient. Thus we provide new approximate models for TCP and also obtain new closed form expressions of mean window size. Using these we obtain the throughput of a TCP connection for the scenario where the queueing delays are non-negligible compared to the overall Round Trip Time (RTT) and also the link utilization is less than one. Our approximate models can be useful not only in the UMTS context but also else where. In the second half of the thesis, we use these approximate models of TCP to provide minimum mean throughput to data connections in UMTS. We also consider real-time applications such as voice and video. These can tolerate a little packet loss (~1%) but require an upper Bound on the delay and delay jitter (≤ 150 ms). Thus if the network provides a constant bandwidth and the received SINR is above a specified threshold ( with a certain probability), QoS for the real-time traffic will be satisfied. The 3G cellular systems are interference limited. Thus wise allocation of power is critical in these systems. Hence we consider the problem of providing end-to-end QoS to different users along with the minimization of the downlink power allocation.

Pushing wireless data traffic onto small cells is important for alleviating congestion in the over-loaded macrocellular network. However, the ultimate potential of such load balancing and its effect on overall system performance is not well understood. With the ongoing deployment of multiple classes of access points (APs) with each class differing in transmit power, employed frequency band, and backhaul capacity, the network is evolving into a complex and “organic” heterogeneous network or HetNet. Resorting to system-level simulations for design insights is increasingly prohibitive with such growing network complexity. The goal of this dissertation is to develop realistic yet tractable frameworks to model and analyze load balancing dynamics while incorporating the heterogeneous nature of these networks. First, this dissertation introduces and analyzes a class of user-AP association strategies, called stationary association, and the resulting association cells for HetNets modeled as stationary point processes. A “Feller-paradox”-like relationship is established between the area of the association cell containing the origin and that of a typical association cell. This chapter also provides a foundation for subsequent chapters, as association strategies directly dictate the load distribution across the network. Second, this dissertation proposes a baseline model to characterize downlink rate and signal-to-interference-plus-noise-ratio (SINR) in an M-band K-tier HetNet with a general weighted path loss based association. Each class of APs is modeled as an independent Poisson point process (PPP) and may differ in deployment density, transmit power, bandwidth (resource), and path loss exponent. It is shown that the optimum fraction of traffic offloaded to maximize SINR coverage is not in general the same as the one that maximizes rate coverage. One of the main outcomes is demonstrating the aggressive of- floading required for out-of-band small cells (like WiFi) as compared to those for in-band (like picocells). To achieve aggressive load balancing, the offloaded users often have much lower downlink SINR than they would on the macrocell, particularly in co-channel small cells. This SINR degradation can be partially alleviated through interference avoidance, for example time or frequency resource partitioning, whereby the macrocell turns off in some fraction of such resources. As the third contribution, this dissertation proposes a tractable framework to analyze joint load balancing and resource partitioning in co-channel HetNets. Fourth, this dissertation investigates the impact of uplink load balancing. Power control and spatial interference correlation complicate the mathixematical analysis for the uplink as compared to the downlink. A novel generative model is proposed to characterize the uplink rate distribution as a function of the association and power control parameters, and used to show the optimal amount of channel inversion increases with the path loss variance in the network. In contrast to the downlink, minimum path loss association is shown to be optimal for uplink rate coverage. Fifth, this dissertation develops a model for characterizing rate distribution in self-backhauled millimeter wave (mmWave) cellular networks and thus generalizes the earlier multi-band offloading framework to the co-existence of current ultra high frequency (UHF) HetNets and mmWave networks. MmWave cellular systems will require high gain directional antennas and dense AP deployments. The analysis shows that in sharp contrast to the interferencelimited nature of UHF cellular networks, mmWave networks are usually noiselimited. As a desirable side effect, high gain antennas yield interference isolation, providing an opportunity to incorporate self-backhauling. For load balancing, the large bandwidth at mmWave makes offloading users, with reliable mmWave links, optimal for rate. / text

L’émergence de nouvelles applications et de nouveaux services (tels que les applications multimédias, la voix-sur-IP, la télévision-sur-IP, la vidéo-sur-demande, etc.) et le besoin croissant de mobilité des utilisateurs entrainent une demande de bande passante de plus en plus croissante et une difficulté dans sa gestion dans les réseaux cellulaires sans fil (WCNs), causant une dégradation de la qualité de service. Ainsi, dans cette thèse, nous nous intéressons à la gestion des ressources, plus précisément à la bande passante, dans les WCNs. Dans une première partie de la thèse, nous nous concentrons sur la prédiction de la mobilité des utilisateurs des WCNs. Dans ce contexte, nous proposons un modèle de prédiction de la mobilité, relativement précis qui permet de prédire la destination finale ou intermédiaire et, par la suite, les chemins des utilisateurs mobiles vers leur destination prédite. Ce modèle se base sur : (a) les habitudes de l’utilisateur en terme de déplacements (filtrées selon le type de jour et le moment de la journée) ; (b) le déplacement courant de l’utilisateur ; (c) la connaissance de l’utilisateur ; (d) la direction vers une destination estimée ; et (e) la structure spatiale de la zone de déplacement. Les résultats de simulation montrent que ce modèle donne une précision largement meilleure aux approches existantes. Dans la deuxième partie de cette thèse, nous nous intéressons au contrôle d’admission et à la gestion de la bande passante dans les WCNs. En effet, nous proposons une approche de gestion de la bande passante comprenant : (1) une approche d’estimation du temps de transfert intercellulaire prenant en compte la densité de la zone de déplacement en terme d’utilisateurs, les caractéristiques de mobilité des utilisateurs et les feux tricolores ; (2) une approche d’estimation de la bande passante disponible à l’avance dans les cellules prenant en compte les exigences en bande passante et la durée de vie des sessions en cours ; et (3) une approche de réservation passive de bande passante dans les cellules qui seront visitées pour les sessions en cours et de contrôle d’admission des demandes de nouvelles sessions prenant en compte la mobilité des utilisateurs et le comportement des cellules. Les résultats de simulation indiquent que cette approche réduit largement les ruptures abruptes de sessions en cours, offre un taux de refus de nouvelles demandes de connexion acceptable et un taux élevé d’utilisation de la bande passante. Dans la troisième partie de la thèse, nous nous penchons sur la principale limite de la première et deuxième parties de la thèse, à savoir l’évolutivité (selon le nombre d’utilisateurs) et proposons une plateforme qui intègre des modèles de prédiction de mobilité avec des modèles de prédiction de la bande passante disponible. En effet, dans les deux parties précédentes de la thèse, les prédictions de la mobilité sont effectuées pour chaque utilisateur. Ainsi, pour rendre notre proposition de plateforme évolutive, nous proposons des modèles de prédiction de mobilité par groupe d’utilisateurs en nous basant sur : (a) les profils des utilisateurs (c’est-à-dire leur préférence en termes de caractéristiques de route) ; (b) l’état du trafic routier et le comportement des utilisateurs ; et (c) la structure spatiale de la zone de déplacement. Les résultats de simulation montrent que la plateforme proposée améliore la performance du réseau comparée aux plateformes existantes qui proposent des modèles de prédiction de la mobilité par groupe d’utilisateurs pour la réservation de bande passante. / The emergence of new applications and services (e.g., multimedia applications, voice over IP and IPTV) and the growing need for mobility of users cause more and more growth of bandwidth demand and a difficulty of its management in Wireless Cellular Networks (WCNs). In this thesis, we are interested in resources management, specifically the bandwidth, in WCNs. In the first part of the thesis, we study the user mobility prediction that is one of key to guarantee efficient management of available bandwidth. In this context, we propose a relatively accurate mobility prediction model that allows predicting final or intermediate destinations and subsequently mobility paths of mobile users to reach these predicted destinations. This model takes into account (a) user’s habits in terms of movements (filtered according to the type of day and the time of the day); (b) user's current movement; (c) user’s contextual knowledge; (d) direction from current location to estimated destination; and (e) spatial conceptual maps. Simulation results show that the proposed model provides good accuracy compared to existing models in the literature. In the second part of the thesis, we focus on call admission control and bandwidth management in WCNs. Indeed, we propose an efficient bandwidth utilization scheme that consists of three schemes: (1) handoff time estimation scheme that considers navigation zone density in term of users, users’ mobility characteristics and traffic light scheduling; (2) available bandwidth estimation scheme that estimates bandwidth available in the cells that considers required bandwidth and lifetime of ongoing sessions; and (3) passive bandwidth reservation scheme that passively reserves bandwidth in cells expected to be visited by ongoing sessions and call admission control scheme for new call requests that considers the behavior of an individual user and the behavior of cells. Simulation results show that the proposed scheme reduces considerably the handoff call dropping rate while maintaining acceptable new call blocking rate and provides high bandwidth utilization rate. In the third part of the thesis, we focus on the main limitation of the first and second part of the thesis which is the scalability (with the number of users) and propose a framework, together with schemes, that integrates mobility prediction models with bandwidth availability prediction models. Indeed, in the two first contributions of the thesis, mobility prediction schemes process individual user requests. Thus, to make the proposed framework scalable, we propose group-based mobility prediction schemes that predict mobility for a group of users (not only for a single user) based on users’ profiles (i.e., their preference in terms of road characteristics), state of road traffic and users behaviors on roads and spatial conceptual maps. Simulation results show that the proposed framework improves the network performance compared to existing schemes which propose aggregate mobility prediction bandwidth reservation models.

O crescente aumento da demanda de tráfego nas redes celulares vem aumentando a necessidade de uma melhor utilização dos recursos do sistema, já que sua expansão é custosa. Nas estações rádio base (ERB), a disponibilidade de largura de faixa de freqüências é limitada e desta maneira, em uma rede de comunicação móvel celular, o controle de admissão de chamadas exerce grande influência no desempenho do sistema, pois determina a utilização de banda das ERBs e se uma determinada quantidade de recursos (banda) será alocado ou não para uma determinada chamada. O desempenho da rede pode ser atrelado a determinados parâmetros, como a probabilidade de bloqueio de novas chamadas, probabilidade de bloqueio de chamadas handoff e a utilização de banda da rede. Este trabalho propõe um controle de admissão de chamadas que, no atendimento de uma chamada, faz o empréstimo de banda de chamadas em andamento na célula no caso de banda insuficiente. O sistema adota um mecanismo heurístico que determina a banda disponível para novas chamadas conforme os valores de certos parâmetros do sistema. O empréstimo de banda é realizado em chamadas em andamento nas células até níveis mínimos estabelecidos para cada tipo de chamada, que se diferenciam pelas necessidades de banda de cada uma. O algoritmo foi aplicado às bandas e características de uma rede de terceira geração (3G), que possui chamadas de voz, videoconferência, interação multimídia, e-mail, downloads e transferência de arquivos e a uma rede GSM/GPRS (global system for mobile communications/ general packet radio service), que possui chamadas de voz e de dados. Os resultados mostram melhorias na probabilidade de bloqueio de novas chamadas, probabilidade de bloqueio de handoff e na utilização de banda do sistema. / The recent growth in traffic loads in cellular networks has seen the need for a better use of system resources as its expansion is expensive. In the base transceiver station (BTS), the bandwidth availability is limited. Thus, in cellular networks the call admission control greatly influences the system performance because it determines the bandwidth use of the BTSs and if an amount of resources will or will not be allocated to a call. The network performance can be evaluated by parameters such as blocking probability of new calls, dropping probability of handoff calls and bandwidth use. This work proposes a call admission control that carries out the bandwidth borrowing when a call arrives and there is not enough bandwidth. The system makes use of a heuristic mechanism that determines the available bandwidth for the new calls according to some parameter values of the system. The bandwidth borrowing is applied to the cell ongoing calls until the minimum levels for each type are met. The algorithm was applied to the bandwidths and characteristics of a third generation cellular network, which supports voice calls, videoconference, multimedia interaction, e-mails, downloads and file transfers. It was also applied to a GSM/GPRS (global system for mobile communications/ general packet radio service), which supports voice and data calls. The results show improvements in the blocking probability of new calls, dropping probability of handoff calls and in the bandwidth use of the system.

Orientador: Celso de Almeida / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-19T04:16:05Z (GMT). No. of bitstreams: 1 MoyaOsorio_DianaPamela_M.pdf: 1282063 bytes, checksum: 0bd5600058b4051f103c6622f93e5cc4 (MD5) Previous issue date: 2011 / Resumo: Na próxima geração de redes celulares, a ICC (Interferência de Co-Canal ) constitui a causa principal para a degradação do desempenho, especialmente para os usuários na borda da célula, o que é um grande obstáculo para alcançar amplas áreas de cobertura e alta eficiência espectral. Neste trabalho, o desempenho do enlace reverso de uma rede celular em termos da eficiência espectral média e na presença de ICC é avaliado mediante uma simulação semi-analítica. Foram considerados dois cenários, canal AWGN(Additive White Gaussian Noise) e canal com desvanecimento plano do tipo Rayleigh, além de um modelo de propagação com perda de percurso exponencial. A avaliação é feita para fatores de reuso clássicos e modulações M-QAM (Multi-Level Quadrature Amplitude Modulation). Considera-se também uma estratégia de controle de potência e modulação adaptativa baseada na SIR (Signal-to-Noise Interference) e a BER (Bit Error Rate). Adicionalmente, é analisada a utilização de arranjo de antenas para diminuição dos efeitos da interferência e técnicas de diversidade para mitigação dos efeitos do desvanecimento / Abstract: In the next generation of wireless cellular networks, the CCI (Co-Channel Interference) constitutes the primary cause of performance degradation, specially for cell edge users, which is a big obstacle to attain wide area coverage and high spectral efficiency. In this work, the performance of the uplink of cellular networks in terms of the mean spectral efficiency is evaluated through a semi-analytical simulation, by considering the presence of CCI. It was considered two sceneries, an AWGN (Additive White Gaussian Noise) channel and a Rayleigh flat fading channel, as well as an exponential pathloss propagation model. The evaluation is done for classical reuse factors and M-QAM (Multi-Level Quadrature Amplitude Modulation) modulations. It is also considered a power control strategy and adaptive modulation based on the SIR (Signal-to-Noise Interference) and BER (Bit Error Rate). Besides, it is analyzed the utilization of an antenna array to reduce the interference effects and diversity techniques to mitigate fading effects / Mestrado / Telecomunicações e Telemática / Mestre em Engenharia Elétrica