Status-Seeking in Hedonic Games with Heterogeneous Players

We study hedonic games with heterogeneous player types that reflect her nationality, ethnic background, or skill type. Agents' preferences are dictated by status-seeking where status can be either local or global. The two dimensions of status define the two components of a generalized constant elasticity of substitution utility function. In this setting, we characterize the core as a function of the utility's parameter values and show that in all cases the corresponding cores are non-empty. We further discuss the core stable outcomes in terms of their segregating versus integrating properties.Coalitions, Core, Stability, Status-seeking

Distributed Caching in Small Cell Networks

The dense deployment of small cells in indoor and outdoor areas contributes mainly in increasing the capacity of cellular networks. On the other hand, the high number of deployed base stations coupled with the increasing growth of data traffic have prompted the apparition of base stations fi tted with storage capacity to avoid network saturation. The storage devices are used as caching units to overcome the limited backhaul capacity in small cells networks (SCNs). Extending the concept of storage to SCNs, gives rise to many new challenges related to the specific characteristics of these networks such as the heterogeneity of the base stations. Formulating the caching problem while taking into account all these specific characteristics with the aim to satisfy the users expectations result in combinatorial optimization problems. However, classical optimization tools do not ensure the optimality of the provided solutions or often the proposed algorithms have an exponential complexity. While most of the existing works are based on the classical optimization tools, in this thesis, we explore another approach to provide a practical solution for the caching problem. In particular, we focus on matching theory which is a game theoretic approach that provides mathematical tools to formulate, analyze and understand scenarios between sets of players. We model the caching problem as a one-to-one matching game between a set of files and a set of base stations and then, we propose an iterative extension of the deferred acceptance algorithm that needs a stable and optimal matching between the two sets. The experimental results show that the proposed algorithm reduces the backhaul load by 10-15 % compared to a random caching algorithm

The edge cloud: A holistic view of communication, computation and caching

The evolution of communication networks shows a clear shift of focus from just improving the communications aspects to enabling new important services, from Industry 4.0 to automated driving, virtual/augmented reality, Internet of Things (IoT), and so on. This trend is evident in the roadmap planned for the deployment of the fifth generation (5G) communication networks. This ambitious goal requires a paradigm shift towards a vision that looks at communication, computation and caching (3C) resources as three components of a single holistic system. The further step is to bring these 3C resources closer to the mobile user, at the edge of the network, to enable very low latency and high reliability services. The scope of this chapter is to show that signal processing techniques can play a key role in this new vision. In particular, we motivate the joint optimization of 3C resources. Then we show how graph-based representations can play a key role in building effective learning methods and devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing: Principles and Applications", P. Djuric and C. Richard Eds., Academic Press, Elsevier, 201

Allocations de ressources dans les réseaux sans fils énergétiquement efficaces.

In this thesis, we investigate two techniques used for enhancing the energy orspectral efficiency of the network. In the first part of the thesis, we propose tocombine the network future context prediction capabilities with the well-knownlatency vs. energy efficiency tradeoff. In that sense, we consider a proactivedelay-tolerant scheduling problem. In this problem, the objective consists ofdefining the optimal power strategies of a set of competing users, which minimizesthe individual power consumption, while ensuring a complete requestedtransmission before a given deadline. We first investigate the single user versionof the problem, which serves as a preliminary to the concepts of delay tolerance,proactive scheduling, power control and optimization, used through the first halfof this thesis. We then investigate the extension of the problem to a multiusercontext. The conducted analysis of the multiuser optimization problem leads toa non-cooperative dynamic game, which has an inherent mathematical complexity.In order to address this complexity issue, we propose to exploit the recenttheoretical results from the Mean Field Game theory, in order to transitionto a more tractable game with lower complexity. The numerical simulationsprovided demonstrate that the power strategies returned by the Mean FieldGame closely approach the optimal power strategies when it can be computed(e.g. in constant channels scenarios), and outperform the reference heuristicsin more complex scenarios where the optimal power strategies can not be easilycomputed.In the second half of the thesis, we investigate a dual problem to the previousoptimization problem, namely, we seek to optimize the total spectral efficiencyof the system, in a constant short-term power configuration. To do so, we proposeto exploit the recent advances in interference classification. the conductedanalysis reveals that the system benefits from adapting the interference processingtechniques and spectral efficiencies used by each pair of Access Point (AP) and User Equipment (UE). The performance gains offered by interferenceclassification can also be enhanced by considering two improvements. First, wepropose to define the optimal groups of interferers: the interferers in a samegroup transmit over the same spectral resources and thus interfere, but can processinterference according to interference classification. Second, we define theconcept of ’Virtual Handover’: when interference classification is considered,the optimal Access Point for a user is not necessarily the one providing themaximal SNR. For this reason, defining the AP-UE assignments makes sensewhen interference classification is considered. The optimization process is thenthreefold: we must define the optimal i) interference processing technique andspectral efficiencies used by each AP-UE pair in the system; ii) the matching ofinterferers transmitting over the same spectral resources; and iii) define the optimalAP-UE assignments. Matching and interference classification algorithmsare extensively detailed in this thesis and numerical simulations are also provided,demonstrating the performance gain offered by the threefold optimizationprocedure compared to reference scenarios where interference is either avoidedwith orthogonalization or treated as noise exclusively.Dans le cadre de cette thèse, nous nous intéressons plus particulièrement àdeux techniques permettant d’améliorer l’efficacité énergétique ou spectrale desréseaux sans fil. Dans la première partie de cette thèse, nous proposons de combinerles capacités de prédictions du contexte futur de transmission au classiqueet connu tradeoff latence - efficacité énergétique, amenant à ce que l’on nommeraun réseau proactif tolérant à la latence. L’objectif dans ce genre de problèmesconsiste à définir des politiques de transmissions optimales pour un ensembled’utilisateur, qui garantissent à chacun de pouvoir accomplir une transmissionavant un certain délai, tout en minimisant la puissance totale consommée auniveau de chaque utilisateur. Nous considérons dans un premier temps le problèmemono-utilisateur, qui permet alors d’introduire les concepts de tolérance àla latence, d’optimisation et de contrôle de puissance qui sont utilisés dans lapremière partie de cette thèse. L’extension à un système multi-utilisateurs estensuite considérée. L’analyse révèle alors que l’optimisation multi-utilisateurpose problème du fait de sa complexité mathématique. Mais cette complexitépeut néanmoins être contournée grâce aux récentes avancées dans le domainede la théorie des jeux à champs moyens, théorie qui permet de transiter d’unjeu multi-utilisateur, vers un jeu à champ moyen, à plus faible complexité. Lessimulations numériques démontrent que les stratégies de puissance retournéespar l’approche jeu à champ moyen approchent notablement les stratégies optimaleslorsqu’elles peuvent être calculées, et dépassent les performances desheuristiques communes, lorsque l’optimum n’est plus calculable, comme c’est lecas lorsque le canal varie au cours du temps.Dans la seconde partie de cettethèse, nous investiguons un possible problème dual au problème précédent. Plusspécifiquement, nous considérons une approche d’optimisation d’efficacité spectrale,à configuration de puissance constante. Pour ce faire, nous proposonsalors d’étudier l’impact sur le réseau des récentes avancées en classification d’interférence.L’analyse conduite révèle que le système peut bénéficier d’uneadaptation des traitements d’interférence faits à chaque récepteur. Ces gainsobservés peuvent également être améliorés par deux altérations de la démarched’optimisation. La première propose de redéfinir les groupes d’interféreurs decellules concurrentes, supposés transmettre sur les mêmes ressources spectrales.L’objectif étant alors de former des paires d’interféreurs “amis”, capables detraiter efficacement leurs interférences réciproques. La seconde altération portele nom de “Virtual Handover” : lorsque la classification d’interférence est considérée,l’access point offrant le meilleur SNR n’est plus nécessairement le meilleuraccess point auquel assigner un utilisateur. Pour cette raison, il est donc nécessairede laisser la possibilité au système de pouvoir choisir par lui-même la façondont il procède aux assignations des utilisateurs. Le processus d’optimisationse décompose donc en trois parties : i) Définir les coalitions d’utilisateurs assignésà chaque access point ; ii) Définir les groupes d’interféreurs transmettantsur chaque ressource spectrale ; et iii) Définir les stratégies de transmissionet les traitements d’interférences optimaux. L’objectif de l’optimisationest alors de maximiser l’efficacité spectrale totale du système après traitementde l’interférence. Les différents algorithmes utilisés pour résoudre, étape parétape, l’optimisation globale du système sont détaillés. Enfin, des simulationsnumériques permettent de mettre en évidence les gains de performance potentielsofferts par notre démarche d’optimisation

Joint Optimization of Resource Allocation and User Association in Multi-Frequency Cellular Networks Assisted by RIS

Due to the development of communication technology and the rise of user network demand, a reasonable resource allocation for wireless networks is the key to guaranteeing regular operation and improving system performance. Various frequency bands exist in the natural network environment, and heterogeneous cellular network (HCN) has become a hot topic for current research. Meanwhile, Reconfigurable Intelligent Surface (RIS) has become a key technology for developing next-generation wireless networks. By modifying the phase of the incident signal arriving at the RIS surface, RIS can improve the signal quality at the receiver and reduce co-channel interference. In this paper, we develop a RIS-assisted HCN model for a multi-base station (BS) multi-frequency network, which includes 4G, 5G, millimeter wave (mmwave), and terahertz networks, and considers the case of multiple network coverage users, which is more in line with the realistic network characteristics and the concept of 6G networks. We propose the optimization objective of maximizing the system sum rate, which is decomposed into two subproblems, i.e., the user resource allocation and the phase shift optimization problem of RIS components. Due to the NP-hard and coupling relationship, we use the block coordinate descent (BCD) method to alternately optimize the local solutions of the coalition game and the local discrete phase search algorithm to obtain the global solution. In contrast, most previous studies have used the coalition game algorithm to solve the resource allocation problem alone. Simulation results show that the algorithm performs better than the rest of the algorithms, effectively improves the system sum rate, and achieves performance close to the optimal solution of the traversal algorithm with low complexity.Comment: 18 page