Network Selection and Resource Allocation Games for Wireless Access Networks

Wireless access networks are often characterized by the interaction of different end users, communication technologies, and network operators. This paper analyzes the dynamics among these "actors" by focusing on the processes of wireless network selection, where end users may choose among multiple available access networks to get connectivity, and resource allocation, where network operators may set their radio resources to provide connectivity. The interaction among end users is modeled as a non-cooperative congestion game where players (end users) selfishly select the access network that minimizes their perceived selection cost. A method based on mathematical programming is proposed to find Nash equilibria and characterize their optimality under three cost functions, which are representative of different technological scenarios. System level simulations are then used to evaluate the actual throughput and fairness of the equilibrium points. The interaction among end users and network operators is then assessed through a two-stage multi-leader/multi-follower game, where network operators (leaders) play in the first stage by properly setting the radio resources to maximize their users, and end users (followers) play in the second stage the aforementioned network selection game. The existence of exact and approximated subgame perfect Nash equilibria of the two-stage game is thoroughly assessed and numerical results are provided on the "quality" of such equilibria

Performance of wireless heterogeneous networks with always-best-connected users

International audienceWe consider a geographic area covered by two wireless networks. Assuming delay-sensitive users, we study the loss of efficiency of the user equilibrium (the Price of Anarchy) in terms of total delay, with M/M/l delay functions on each network. The user equilibrium is proved to be less efficient when the network is very heterogeneous, i.e. the two networks have different capacities. In order to elicit coordination among users, we suggest to use marginal cost pricing. We investigate the computation of the optimal taxes to use, and give several arguments in favor of the technical feasibility of such a scheme. Applying taxes therefore seems particularly well-suited to improve the overall performance of a network selection game with selfish users

Game theoretic models for resource sharing in wireless networks

Wireless communications have been recently characterized by rapid proliferation of wireless networks, impressive growth of standard and technologies, evolution of the end-user terminals, and increasing demand in the wireless spectrum. New, more flexible schemes for the management of the available resources, from both the user and the network side, are necessary in order to improve the efficiency in the usage of the available resources.This work aims at shedding light on the performance modeling of radio resource sharing/allocation situations. Since, in general, the quality of service perceived by a system (e.g., user, network) strictly depends on the behavior of the other entities, and the involved interactions are mainly competitive, this work introduces a framework based on non–cooperative game theoretic tools. Furthermore, non–cooperative game theory is suitable in distributed networks, where control and management are inherently decentralized.First, we consider the case in which many users have to make decisions on which wireless access point to connect to. In this scenario, the quality perceived by the users mainly depends on the number of other users choosing the very same accessing opportunity. In this context, we also consider two–stage games where network make decisions on how to use the available resources, and users react to this selecting the network that maximizes their satisfaction. Then, we refer to the problem of spectrum sharing, where users directly compete for portions of the available spectrum. Finally, we provide a more complex model where the users utility function is based on the Shannon rate. The aim of this second part is to provide a better representation of the satisfaction perceived by the users, i.e., in terms of achievable throughput. Due to the complexity of the game model, we first provide a complete analytical analysis of the two–user case. Then, we extend the model to the N–user case. We mainly analyze this game through simulations. Finally, inspired by the results obtained numerically, we introduce stochastic geometry in the analysis of spectrum games in order to predict the performance of the game in large networks.Ph.D., Electrical Engineering -- Drexel University, 201