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In recent years, many approaches and techniques have been explored for the optimization of energy usage in Wireless Sensor Networks (WSN). It is well recognized that a proper energy consumption model is the foundation for developing and evaluating a power management scheme in WSN. In this paper, we propose a new complete information Markov Decision Process (MDP) model to characterize sensors energy...
In this paper we study mobility effect and power saving in cognitive radio networks using mean field games. We consider two types of users: primary and secondary users. When active, each secondary transmitter-receiver uses carrier sensing and is subject to long-term energy constraint. We formulate the interaction between primary user and large number of secondary users as an hierarchical mean field...
We study a class of mean field stochastic games in discrete time and continuous state space. Each player has its own individual state evolution described by a stochastic difference equation which depends not only on the control of the corresponding player but also on the states of the other players. Considering the specific structure of aggregate drift and diffusion terms, we use classical asymptotic...
In this paper we establish a relationship between chemical dynamics and mean field game dynamics. We show that chemical reaction networks can be studied using noisy mean field limits. We provide deterministic, noisy and switching mean field limits and illustrate them with numerical examples.
We study the mobile association problem: we determine the cells corresponding to each base station, i.e, the locations at which intelligent mobile terminals prefer to connect to a given base station rather than to others. This paper proposes a new approach based on optimal transport theory to characterize the solution based on previous works on fluid approximations. We characterize the optimal solution...
This paper is concerned with the concept of equilibrium and quality of service (QoS) provisioning in self-configuring wireless networks with non-cooperative radio devices (RD). In contrast with the Nash equilibrium (NE), where RDs are interested in selfishly maximizing its QoS, we present a concept of equilibrium, named satisfaction equilibrium (SE), where RDs are interested only in guaranteing a...
Learning algorithms are essential for the applications of game theory in a networking environment. In dynamic and decentralized settings where the traffic, topology and channel states may vary over time and the communication between agents is impractical, it is important to formulate and study games of incomplete information and fully distributed learning algorithms which for each agent requires a...
In this paper, we consider a network composed of several interfering transmitter-receiver pairs where all the terminals are equipped with multiple antennas. The problem of finding the precoding matrices minimizing the outage probabilities is analyzed using a game theoretical framework under the assumption of slow fading links and non-cooperative transmissions. An analytical solution of this game is...
In this paper, we focus on robust power allocation strategies, against the imperfectness of the channel state information at the transmitters in discrete power allocation games. Considering imperfectness in term of payoff measurement and time delays at the transmitters, we propose a Delayed COmbined fully DIstributed Payoff and Strategy Reinforcement Learning (Delayed-CODIPAS-RL) in which each transmitter...
We consider the uplink mobile association game with a large number of mobile terminals. Traditional techniques consider the discrete modelization but these models lead to high combinatorial complexities. Thanks to optimal transport theory we are able to characterize the cell formation as well as the equilibrium properties of the network where intelligent mobile terminals decide by themselves to which...
Considering the interaction through mutual interference of the different radio devices, the channel selection (CS) problem in decentralized parallel multiple access channels can be modeled by strategic-form games. Here, we show that the CS problem is a potential game (PG) and thus the fictitious play (FP) converges to a Nash equilibrium (NE) either in pure or mixed strategies. Using a 2-player 2-channel...
This paper aims to contribute to bridge the gap between existing theoretical results in distributed radio resource allocation policies based on equilibria in games (assuming complete information and rational players) and practical design of signal processing algorithms for self-configuring wireless networks. For this purpose, the framework of learning theory in games is exploited. Here, a new learning...
The purpose of this paper is to show how key concepts from control theory, game theory, and mean field theory can be exploited to design joint control-allocation policies in cognitive wireless networks. One of the key features of the proposed approach is that transmitters (which are assumed to be cognitive and autonomous decisionnally speaking) have a certain knowledge of the channel evolution law...
This paper considers the problem of cooperative power control in distributed small cell wireless networks. We introduce a novel framework, based on repeated games, the interactions of the different transmit base stations in the downlink. By exploiting the specific structure of the game, we show that we can improve the system performance by selecting the Pareto optimal solution as well as reduce the...
In this paper, we study a network security configuration problem. More specifically, we consider distributed intrusion detection systems in a network subject to possible simultaneous attacks launched by a number of attackers. We formulate an N + M-person nonzero-sum stochastic game to capture the interactions among detection systems in the network as well as their interactions against exogenous intruders...
In this paper, we propose an evolutionary game-theoretic framework for hybrid additive white Gaussian noise multiple access channels. We consider a communication system consisting of multiple users and multiple receivers, where each user chooses a rate and splits it over the receivers. Users have coupled constraints determined by the capacity regions. We show the existence of Nash equilibrium under...
We consider evolving games with finite number of players, in which each player interacts with other randomly selected players. The types and actions of each player in an interaction together determine the instantaneous payoff for all involved players. They also determine the rate of transition between type-actions. We provide a rigorous derivation of the asymptotic behavior of this system as the size...
We study a dynamic random access game with a finite number of opportunities for transmission and with energy constraints. We provide sufficient conditions for feasible strategies and for existence of Nash-Pareto solutions and show that finding Nash-Pareto policies of the dynamic random access game is equivalent to partitioning the set of time slot opportunities with constraints into a set of terminals...
In this paper, we formulate an evolutionary multiple access channel game with continuous-variable actions and coupled rate constraints. We characterize Nash equilibria of the game and show that the pure Nash equilibria are Pareto optimal and also resilient to deviations by coalitions of any size, i.e., they are strong equilibria. We use the concepts of price of anarchy and strong price of anarchy...
We introduce Markov decision evolutionary games with N players, in which each individual in a large population interacts with other randomly selected players. The states and actions of each player in an interaction together determine the instantaneous payoff for all involved players. They also determine the transition probabilities to move to the next state. Each individual wishes to maximize the...
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