Game Theory in Wireless and Communication Networks: Theory, Models, and Applications

By Zhu Han, Dusist Niyato, Walid Saad, Tamer Bąar, Are Hjrungnes. 2011

529 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/c2330b48bf56d7262ccc17c3f737ca6d97ec2595

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In Computer Networks and Communications

Citations in the corpus

Game-theoretic resource allocation methods for device-to-device communication (2014, background): “Distributed radio resource allocation algorithm can be designed based on game theory [13].”
Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems (2018, background): “er’s actions. In the following we analyze the interdependence of the attacker and the AV’s actions to each other and their previous actions and we formulate such problem in a game-theoretic framework [19]. To this end, we ﬁrst derive the impact of past attacker and AV actions on their future actions”
Interference Management for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach (2018, methodology): “Based on the proposed deep ESN architecture and update rule, we next introduce a multi-agent deep RL framework that the UAVs can use to learn an SPNE in behavioral strategies for the game G .”
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems (2018, background): “As such, along with the use of machine learning, game theory [196], [197] will provide important foundations for distributed decision making in UAV-based wireless networks.”
Robust Power Management via Learning and Game Design (2021, methodology): “Following the standard convention in wireless communications literature (Han et al. 2011), the transmission power pi for transmitter i is assumed to lie in a compact interval Pi = [0, p i ].”
Matching theory for future wireless networks: fundamentals and applications (2014, background): “The basic idea is to cast the matching problem as a stochastic game and then explore the rich literature on dynamic game theory [1] to solve this problem while ensuring that the solution will converge to a two-sided stable matching rather than a classical Nash equilibrium.”
Game-Theoretic Methods for the Smart Grid: An Overview of Microgrid Systems, Demand-Side Management, and Smart Grid Communications (2012, unknown): “igniﬁcant attention recently in game theory and multi-agent learning (see [6] and references therein).These approaches have also been recently shown to perform well in practical communication systems [2], [9] and, hence, it is natural to leverage their use into practical control systems such as the ”
A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks (2018, methodology): “…various assumptions about the ofﬂoading scenarios (e.g., multi-leader vs. single leaders), different approaches such as nonlinear optimization formulation or best response-based equilibrium searching are applied to each layer’s sub-problem in the manner of backward induction [142, Chapter 3.4.2].”