Repeated game theory as a framework for algorithm development in communication networks

This article presents a tutorial on how to use repeated game theory as a framework for algorithm development in communication networks. The article starts by introducing the basis of one-stage games and how the outcome of such games can be predicted, through iterative elimination and Nash equilibrium. In communication networks, however, not all problems can be modeled using one-stage games. Some problems can be better modeled through multi-stage games, as many problems in communication networks consist of several iterations or decisions that need to be made over time. Of all the multi-stage games, the infinite-horizon repeated games were chosen to be the focus in this tutorial, because optimal equilibrium settings can be achieved, contrarily to the suboptimal equilibria achieved in other types of game. With the theoretical concepts introduced, it is then shown how the developed game theoretical model, and devised equilibrium, can be used as a basis for the behavior of an algorithm, which is supposed to solve a particular problem and will be running at specific network devices. Copyright (C) 2015 John Wiley & Sons, Ltd.FCT (Foundation for Science and Technology) of Portugal within CEOT (Center for Electronic, Optoelectronic and Telecommunications)info:eu-repo/semantics/publishedVersio

Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference

Cloud Compute-and-Forward with Relay Cooperation

We study a cloud network with M distributed receiving antennas and L users, which transmit their messages towards a centralized decoder (CD), where M>=L. We consider that the cloud network applies the Compute-and-Forward (C&F) protocol, where L antennas/relays are selected to decode integer equations of the transmitted messages. In this work, we focus on the best relay selection and the optimization of the Physical-Layer Network Coding (PNC) at the relays, aiming at the throughput maximization of the network. Existing literature optimizes PNC with respect to the maximization of the minimum rate among users. The proposed strategy maximizes the sum rate of the users allowing nonsymmetric rates, while the optimal solution is explored with the aid of the Pareto frontier. The problem of relay selection is matched to a coalition formation game, where the relays and the CD cooperate in order to maximize their profit. Efficient coalition formation algorithms are proposed, which perform joint relay selection and PNC optimization. Simulation results show that a considerable improvement is achieved compared to existing results, both in terms of the network sum rate and the players' profits.Comment: Submitted to IEEE Transactions on Wireless Communication

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201