Cette thèse se focalise sur les jeux de potentiel et une généralisation d'un jeu d'ordonnancement dans un graphe que nous avons appelé jeu de placement.Dans ce jeu, le coût d'un joueur est impacté par son voisinage.Nous pouvons illustrer cela avec un exemple : le placement de joueurs dans un train, pour lesquels la présence de voisins directs influe sur le bien-être.Les résultats de cette thèse se divisent en deux parties.Tout d'abord, nous étudions ces jeux en considérant l'existence et les propriétés de structure des équilibres.Nous nous posons la question fondamentale de savoir s'il existe des équilibres de Nash dans le jeu de placement.Si tel est le cas, nous tachons de déterminer si ces équilibres sont facilement calculables.Dans le cas où il n'existe pas d'équilibre nous prouvons la NP-complétude du problème.Dans un second temps nous nous intéressons à la notion de calcul distribué d'équilibre de Nash dans des jeux de placement.En particulier nous considérons un jeu basé sur le problème de Max-Cut, qui a été plus étudié en théorie des graphes.Cela nous a permis d'étendre nos travaux à une application aux réseaux mobiles pour la gestion d'interférences dans les réseaux sans fils.Nous avons pu, pour les différents jeux, mettre en place des algorithmes distribués de calcul d'équilibres et étudier leur convergence.Parallèlement, nous avons étendu les travaux de Max-Cut à un problème de sélection d'offre de qualité de service parmi divers fournisseurs d'accès.Nous comparons les performances d'algorithmes de calcul distribué d'équilibres et de minimisation de regret. / This thesis focuses on potential games and a generalized load balancing game in a graph we called placement game.In this game, the cost of a player is affected by its neighbors.We can illustrate this with an example: the placement of players on a train, where the presence of direct neighbors affects their well-being.The results of this thesis are divided into two parts.First, we study these games considering the existence and structural properties of equilibria.We ask ourselves the fundamental question of whether there are Nash equilibria in the placement game.If this is the case we aim to determine if they are easily calculable, if there is no such equilibria we prove the NP-completeness of the problem.Secondly we focus on the concept of distributed algorithms to compute Nash equilibria in placement games.In particular we consider a game based on the Max-Cut problem, which has been more frequently studied.This allowed us to expand our work to a mobile network application for managing interference in wireless networks.We were able, for those different games, to implement distributed algorithms to compute equilibria and study their convergence.Meanwhile, we have expanded the Max-Cut works with a selection of QoS offers problem from various network providers.We compare the performance of distributed algorithms and regret minimization.

We consider a wireless communication system in which there are N transmitter-receiver pairs and each transmitter wants to communicate with its corresponding receiver. This is modelled as an interference channel. We propose power allocation algorithms for increasing the sum rate of two and three user interference channels. The channels experience fast fading and there is an average power constraint on each transmitter. In this case receivers use successive decoding under strong interference, instead of treating interference as noise all the time. Next, we u se game theoretic approach for power allocation where each receiver treats interference as noise. Each transmitter-receiver pair aims to maximize its long-term average transmission rate subject to an average power constraint. We formulate a stochastic game for this system in three different scenarios. First, we assume that each user knows all direct and crosslink channel gains. Next, we assume that each user knows channel gains of only the links that are incident on its receiver. Finally, we assume that each use r knows only its own direct link channel gain. In all cases, we formulate the problem of finding the Nash equilibrium(NE) as a variational in equality problem. For the game with complete channel knowledge, we present an algorithm to solve the VI and we provide weaker sufficient conditions for uniqueness of the NE than the sufficient conditions available in the literature. Later, we present a novel heuristic for solving the VI under general channel conditions. We also provide a distributed algorithm to compute Pare to optimal solutions for the proposed games. We use Bayesian learning that guarantees convergence to an Ɛ-Nash equilibrium for the incomplete information game with direct link channel gain knowledge only, that does not require knowledge of the power policies of other users but requires feedback of the interference power values from a receiver to its corresponding transmitter. Later, we consider a more practical scenario in which each transmitter transmits data at a certain rate using a power that depends on the channel gain to its receiver. If a receiver can successfully receive the message, it sends an acknowledgement(ACK), else it sends a negative ACK(NACK). Each user aims to maximize its probability of successful transmission. We formulate this problem as a stochastic game and propose a fully distributed learning algorithm to find a correlated equilibrium(CE). In addition, we use a no regret algorithm to find a coarse correlated equilibrium(CCE) for our power allocation game. We also propose a fully distributed learning algorithm to find a Pareto optimal solution. In general Pareto points do not guarantee fairness among the users. Therefore we also propose an algorithm to compute a Nash bargaining solution which is Pareto optimal and provides fairness among the users. Finally, we extend these results when each transmitter sends data at multiple rates rather than at a fixed rate.