Improving relay based cellular networks performance in highly user congested and emergency situations

PhDRelay based cellular networks (RBCNs) are the technologies that incorporate multi-hop communication into traditional cellular networks. A RBCN can potentially support higher data rates, more stable radio coverage and more dynamic services. In reality, RBCNs still suffer from performance degradation in terms of high user congestion, base station failure and overloading in emergency situations. The focus of this thesis is to explore the potential to improve IEEE802.16j supported RBCN performance in user congestion and emergency situations using adjustments to the RF layer (by antenna adjustments or extensions using multi-hop) and cooperative adjustment algorithms, e.g. based on controlling frequency allocation centrally and using distributed approaches. The first part of this thesis designs and validates network reconfiguration algorithms for RBCN, including a cooperative antenna power control algorithm and a heuristic antenna tilting algorithm. The second part of this thesis investigates centralized and distributed dynamic frequency allocation for higher RBCN frequency efficiency, network resilience, and computation simplicity. It is demonstrated that these benefits mitigate user congestion and base station failure problems significantly. Additionally, interweaving coordinated dynamic frequency allocation and antenna tilting is investigated in order to obtain the benefits of both actions. The third part of this thesis incorporates Delay Tolerate Networking (DTN) technology into RBCN to let users self-organize to connect to functional base station through multi-hops supported by other users. Through the use of DTN, RBCN coverage and performance are improved. This thesis explores the augmentation of DTN routing protocols to let more un-covered users connect to base stations and improve network load balancin

Business scenarios, technical challenges and system requirements - D2.1

Deliverable D2.1 del projecte Europeu OneFIT (ICT-2009-257385)Preprin

A survey on Human Mobility and its applications

Human Mobility has attracted attentions from different fields of studies such as epidemic modeling, traffic engineering, traffic prediction and urban planning. In this survey we review major characteristics of human mobility studies including from trajectory-based studies to studies using graph and network theory. In trajectory-based studies statistical measures such as jump length distribution and radius of gyration are analyzed in order to investigate how people move in their daily life, and if it is possible to model this individual movements and make prediction based on them. Using graph in mobility studies, helps to investigate the dynamic behavior of the system, such as diffusion and flow in the network and makes it easier to estimate how much one part of the network influences another by using metrics like centrality measures. We aim to study population flow in transportation networks using mobility data to derive models and patterns, and to develop new applications in predicting phenomena such as congestion. Human Mobility studies with the new generation of mobility data provided by cellular phone networks, arise new challenges such as data storing, data representation, data analysis and computation complexity. A comparative review of different data types used in current tools and applications of Human Mobility studies leads us to new approaches for dealing with mentioned challenges

Délestage de données en D2D : de la modélisation à la mise en oeuvre

Mobile data traffic is expected to reach 24.3 exabytes by 2019. Accommodating this growth in a traditional way would require major investments in the radio access network. In this thesis, we turn our attention to an unconventional solution: mobile data offloading through device-to-device (D2D) communications. Our first contribution is DROiD, an offloading strategy that exploits the availability of the cellular infrastructure as a feedback channel. DROiD adapts the injection strategy to the pace of the dissemination, resulting at the same time reactive and relatively simple, allowing to save a relevant amount of data traffic even in the case of tight delivery delay constraints.Then, we shift the focus to the gains that D2D communications could bring if coupled with multicast wireless networks. We demonstrate that by employing a wise balance of multicast and D2D communications we can improve both the spectral efficiency and the load in cellular networks. In order to let the network adapt to current conditions, we devise a learning strategy based on the multi-armed bandit algorithm to identify the best mix of multicast and D2D communications. Finally, we investigate the cost models for operators wanting to reward users who cooperate in D2D offloading. We propose separating the notion of seeders (users that carry content but do not distribute it) and forwarders (users that are tasked to distribute content). With the aid of the analytic framework based on Pontryagin's Maximum Principle, we develop an optimal offloading strategy. Results provide us with an insight on the interactions between seeders, forwarders, and the evolution of data dissemination.Le trafic mobile global atteindra 24,3 exa-octets en 2019. Accueillir cette croissance dans les réseaux d’accès radio devient un véritable casse-tête. Nous porterons donc toute notre attention sur l'une des solutions à ce problème : le délestage (offloading) grâce à des communications de dispositif à dispositif (D2D). Notre première contribution est DROiD, une stratégie qui exploite la disponibilité de l'infrastructure cellulaire comme un canal de retour afin de suivre l'évolution de la diffusion d’un contenu. DROiD s’adapte au rythme de la diffusion, permettant d'économiser une quantité élevée de données cellulaires, même dans le cas de contraintes de réception très serrées. Ensuite, nous mettons l'accent sur les gains que les communications D2D pourraient apporter si elles étaient couplées avec les transmissions multicast. Par l’utilisation équilibrée d'un mix de multicast, et de communications D2D, nous pouvons améliorer, à la fois, l'efficacité spectrale ainsi que la charge du réseau. Afin de permettre l’adaptation aux conditions réelles, nous élaborons une stratégie d'apprentissage basée sur l'algorithme dit ‘’bandit manchot’’ pour identifier la meilleure combinaison de communications multicast et D2D. Enfin, nous mettrons en avant des modèles de coûts pour les opérateurs, désireux de récompenser les utilisateurs qui coopèrent dans le délestage D2D. Nous proposons, pour cela, de séparer la notion de seeders (utilisateurs qui transportent contenu, mais ne le distribuent pas) et de forwarders (utilisateurs qui sont chargés de distribuer le contenu). Avec l'aide d’un outil analytique basée sur le principe maximal de Pontryagin, nous développons une stratégie optimale de délestage