Analyse de scénarios de déploiement pour le désengorgement des réseaux mobiles par application de la théorie des jeux

Mobile network connectivity is currently mainly provided by wide area technologies utilizing outdoor-located base stations. However, the provisioning of services consuming more and more data at a higher bit rate requires new network deployment strategies for mobile operators. In this context, a strategic option for the evolution of radio access networks, especially in dense urban areas, is mobile network offloading. Offloading is a win-win solution since both mobile operators and end-users are benefited by such a deployment. In this thesis, we describe several deployment schemes for mobile offloading using femtocells or WiFi. The objective is to evaluate for each offloading solution where and when it should be deployed by mobile operators. To achieve this goal, we have led a qualitative and a game-theoretic analysis which have enabled us to figure out for each of those offloading solutions their strengths and weaknesses as well as their specificities in different competitive environments. The qualitative analysis conducted concludes that femtocells are more likely to be deployed in business areas since they guarantee a better quality of service, meet higher security requirements and are manageable by operators even if they are installed on private places. On the other hand, WiFi networks already exist in most residential areas and consumer customers are familiar with the technology. Thus, it will probably be easy for operators to use these wireless access points to redirect part of their mobile traffic. Finally, the results of game theory propose for each offloading solution a market penetration plan

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Aerial base station placement in temporary-event scenarios

Die Anforderungen an den Netzdatenverkehr sind in den letzten Jahren dramatisch gestiegen, was ein großes Interesse an der Entwicklung neuartiger Lösungen zur Erhöhung der Netzkapazität in Mobilfunknetzen erzeugt hat. Besonderes Augenmerk wurde auf das Problem der Kapazitätsverbesserung bei temporären Veranstaltungen gelegt, bei denen das Umfeld im Wesentlichen dynamisch ist. Um der Dynamik der sich verändernden Umgebung gerecht zu werden und die Bodeninfrastruktur durch zusätzliche Kapazität zu unterstützen, wurde der Einsatz von Luftbasisstationen vorgeschlagen. Die Luftbasisstationen können in der Nähe des Nutzers platziert werden und aufgrund der im Vergleich zur Bodeninfrastruktur höheren Lage die Vorteile der Sichtlinienkommunikation nutzen. Dies reduziert den Pfadverlust und ermöglicht eine höhere Kanalkapazität. Das Optimierungsproblem der Maximierung der Netzkapazität durch die richtige Platzierung von Luftbasisstationen bildet einen Schwerpunkt der Arbeit. Es ist notwendig, das Optimierungsproblem rechtzeitig zu lösen, um auf Veränderungen in der dynamischen Funkumgebung zu reagieren. Die optimale Platzierung von Luftbasisstationen stellt jedoch ein NP-schweres Problem dar, wodurch die Lösung nicht trivial ist. Daher besteht ein Bedarf an schnellen und skalierbaren Optimierungsalgorithmen. Als Erstes wird ein neuartiger Hybrid-Algorithmus (Projected Clustering) vorgeschlagen, der mehrere Lösungen auf der Grundlage der schnellen entfernungsbasierten Kapazitätsapproximierung berechnet und sie auf dem genauen SINR-basierten Kapazitätsmodell bewertet. Dabei werden suboptimale Lösungen vermieden. Als Zweites wird ein neuartiges verteiltes, selbstorganisiertes Framework (AIDA) vorgeschlagen, welches nur lokales Wissen verwendet, den Netzwerkmehraufwand verringert und die Anforderungen an die Kommunikation zwischen Luftbasisstationen lockert. Bei der Formulierung des Platzierungsproblems konnte festgestellt werden, dass Unsicherheiten in Bezug auf die Modellierung der Luft-Bodensignalausbreitung bestehen. Da dieser Aspekt im Rahmen der Analyse eine wichtige Rolle spielt, erfolgte eine Validierung moderner Luft-Bodensignalausbreitungsmodelle, indem reale Messungen gesammelt und das genaueste Modell für die Simulationen ausgewählt wurden.As the traffic demands have grown dramatically in recent years, so has the interest in developing novel solutions that increase the network capacity in cellular networks. The problem of capacity improvement is even more complex when applied to a dynamic environment during a disaster or temporary event. The use of aerial base stations has received much attention in the last ten years as the solution to cope with the dynamics of the changing environment and to supplement the ground infrastructure with extra capacity. Due to higher elevations and possibility to place aerial base stations in close proximity to the user, path loss is significantly smaller in comparison to the ground infrastructure, which in turn enables high data capacity. We are studying the optimization problem of maximizing network capacity by proper placement of aerial base stations. To handle the changes in the dynamic radio environment, it is necessary to promptly solve the optimization problem. However, we show that the optimal placement of aerial base stations is the NP-hard problem and its solution is non-trivial, and thus, there is a need for fast and scalable optimization algorithms. This dissertation investigates how to solve the placement problem efficiently and to support the dynamics of temporary events. First, we propose a novel hybrid algorithm (Projected Clustering), which calculates multiple solutions based on the fast distance-based capacity approximation and evaluates them on the accurate SINR-based capacity model, avoiding sub-optimal solutions. Second, we propose a novel distributed, self-organized framework (AIDA), which conducts a decision-making process using only local knowledge, decreasing the network overhead and relaxing the requirements for communication between aerial base stations. During the formulation of the placement problem, we found that there is still considerable uncertainty with regard to air-to-ground propagation modeling. Since this aspect plays an important role in our analysis, we validated state-of-the-art air-to-ground propagation models by collecting real measurements and chose the most accurate model for the simulations

A Game-theoretic Model for Regulating Freeriding in Subsidy-Based Pervasive Spectrum Sharing Markets

Cellular spectrum is a limited natural resource becoming scarcer at a worrisome rate. To satisfy users\u27 expectation from wireless data services, researchers and practitioners recognized the necessity of more utilization and pervasive sharing of the spectrum. Though scarce, spectrum is underutilized in some areas or within certain operating hours due to the lack of appropriate regulatory policies, static allocation and emerging business challenges. Thus, finding ways to improve the utilization of this resource to make sharing more pervasive is of great importance. There already exists a number of solutions to increase spectrum utilization via increased sharing. Dynamic Spectrum Access (DSA) enables a cellular operator to participate in spectrum sharing in many ways, such as geological database and cognitive radios, but these systems perform spectrum sharing at the secondary level (i.e., the bands are shared if and only if the primary/licensed user is idle) and it is questionable if they will be sufficient to meet the future expectations of the spectral efficiency. Along with the secondary sharing, spectrum sharing among primary users is emerging as a new domain of future mode of pervasive sharing. We call this type of spectrum sharing among primary users as pervasive spectrum sharing (PSS) . However, such spectrum sharing among primary users requires strong incentives to share and ensuring a freeriding-free cellular market. Freeriding in pervasively shared spectrum markets (be it via government subsidies/regulations or self-motivated coalitions among cellular operators) is a real techno-economic challenge to be addressed. In a PSS market, operators will share their resources with primary users of other operators and may sometimes have to block their own primary users in order to attain sharing goals. Small operators with lower quality service may freeride on large operators\u27 infrastructure in such pervasively shared markets. Even worse, since small operators\u27 users may perceive higher-than-expected service quality for a lower fee, this can cause customer loss to the large operators and motivate small operators to continue freeriding with additional earnings from the stolen customers. Thus, freeriding can drive a shared spectrum market to an unhealthy and unstable equilibrium. In this work, we model the freeriding by small operators in shared spectrum markets via a game-theoretic framework. We focus on a performance-based government incentivize scheme and aim to minimize the freeriding issue emerging in such PSS markets. We present insights from the model and discuss policy and regulatory challenges

Applications of Game Theory and Microeconomics in Cognitive Radio and Femtocell Networks

Cognitive radio networks have recently been proposed as a promising approach to overcome the serious problem of spectrum scarcity. Other emerging concept for innovative spectrum utilization is femtocells. Femtocells are low-power and short-range wireless access points installed by the end-user in residential or enterprise environments. A common feature of cognitive radio and femtocells is their two-tier nature involving primary and secondary users (PUs, SUs). While this new paradigm enables innovative alternatives to conventional spectrum management and utilization, it also brings its own technical challenges. A main challenge in cognitive radio is the design of efficient resource (spectrum) trading methods. Game and microeconomics theories provide tools for studying the strategic interactions through rationality and economic benefits between PUs and SUs for effective resource allocation. In this thesis, we investigate some efficient game theoretic and microeconomic approaches to address spectrum trading in cognitive networks. We propose two auction frameworks for shared and exclusive use models. In the first auction mechanism, we consider the shared used model in cognitive radio networks and design a spectrum trading method to maximize the total satisfaction of the SUs and revenue of the Wireless Service Provider (WSP). In the second auction mechanism, we investigate spectrum trading via auction approach for exclusive usage spectrum access model in cognitive radio networks. We consider a realistic valuation function and propose an efficient concurrent Vickrey-Clarke-Grove (VCG) mechanism for non-identical channel allocation among r-minded bidders in two different cases. The realization of cognitive radio networks in practice requires the development of effective spectrum sensing methods. A fundamental question is how much time to allocate for sensing purposes. In the literature on cognitive radio, it is commonly assumed that fixed time durations are assigned for spectrum sensing and data transmission. It is however possible to improve the network performance by finding the best tradeoff between sensing time and throughput. In this thesis, we derive an expression for the total average throughput of the SUs over time-varying fading channels. Then we maximize the total average throughput in terms of sensing time and the number of SUs assigned to cooperatively sense each channel. For practical implementation, we propose a dynamical programming algorithm for joint optimization of sensing time and the number of cooperating SUs for sensing purpose. Simulation results demonstrate that significant improvement in the throughput of SUs is achieved in the case of joint optimization. In the last part of the thesis, we further address the challenge of pricing in oligopoly market for open access femtocell networks. We propose dynamic pricing schemes based on microeconomic and game theoretic approaches such as market equilibrium, Bertrand game, multiple-leader-multiple-follower Stackelberg game. Based on our approaches, the per unit price of spectrum can be determined dynamically and mobile service providers can gain more revenue than fixed pricing scheme. Our proposed methods also provide residential customers more incentives and satisfaction to participate in open access model.1 yea