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Resilient UAV Networks: Solutions and Trends
Published in Fei Hu, Xin-Lin Huang, DongXiu Ou, UAV Swarm Networks, 2020
Zhiyong Xia, Fei Hu, Nathan Jeong, Iftikhar Rasheed
In [4], a representative intrusion detection method based on Bayesian game theory is proposed to secure the UAV network. Bayesian game here means that the game participants do not have complete information about the profit model of its opponent. In this case incomplete information means that the IDS agent does not know the properties of the attacker and the attacker does not know whether its neighboring node uses an IDS or not.
Game Theory
Published in Erchin Serpedin, Thomas Chen, Dinesh Rajan, Mathematical Foundations for SIGNAL PROCESSING, COMMUNICATIONS, AND NETWORKING, 2012
Erik G. Larsson, Eduard Jorswiec
A Bayesian game is described by the quintuple: the set of players (A, B), their strategy spaces, the set of types (TA, TB), the conditional probabilities π, and the utilities. The utilities depend on the strategies as well as on the types. It is assumed that this tuple is common knowledge. In addition, player A knows his own type tA. This common prior assumption plays a central role in Bayesian games: There exists a common a priori probability distribution p, called common prior, if the conditional probabilities p(tB∣tA) and p(tA∣tB) are derived from some probability distribution p on TA × TB by () p(tB|tA)=p(tA,tB)ΣτB∈TBp(tA,tB)andp(tA|tB)=p(tA,tB)ΣτA∈TAp(τA,τB)
Learning under Delayed Measurement
Published in Hamidou Tembine, Distributed Strategic Learning for Wireless Engineers, 2018
The problem of competitive Shannon rate maximization is an important signal-processing problem for power-constrained multiuser systems. It involves solving the power allocation problem for mutually interfering transmitters operating across multiple frequencies. The classical approach to Shannon rate maximization has been finding globally optimal solutions based on waterfilling [50]. However, the major drawback of this approach is that these solutions require centralized control. These solutions are inherently unstable in a competitive multi-user scenario, since a gain in performance for one transmitter may result in a loss of performance for others. Instead, a distributed game-theoretic approach is desirable and is being increasingly considered only over the past decade. The seminal works on competitive Shannon rate maximization use a game-theoretic approach to design a decentralized algorithm for two-user dynamic power control. These works proposed a sequential iterative waterfilling algorithm for reaching the Nash equilibrium in a distributed manner. A Nash equilibrium of the rate-maximization game is a power allocation configuration such that given the power allocations of other transmitters, no transmitter can further increase the achieved information rate unilaterally. However, most of the existing works on power allocation games assume perfect channel state information (CSI). This is a very strong requirement and generally cannot be met by practical wireless systems. The traditional game-theoretic solution for systems with imperfect information is the Bayesian game model, which uses a probabilistic approach to model the uncertainty in the system. However, a Bayesian approach is often intractable and the results strongly depend on the nature of the probability distribution functions. Thus, a relaxation of the use of the initial probability distribution is needed. we propose a robust game approach to solve this problem. The motivations to consider imperfect channel state are the following: The channel state information (CSI), is usually estimated at the receiver by using a training sequence, or semi-blind estimation methods. Obtaining channel state information at the transmitter requires either a feedback channel from the receiver to the transmitter, or exploiting the channel reciprocity such as in time division duplexing systems.While it is a reasonable approximation to assume perfect channel state information at the receiver, usually channel state information at the transmitter cannot be assumed perfect, due to many factors such as inaccurate channel estimation, erroneous or outdated feedback, and time delays or frequency offsets between the reciprocal channels.Therefore, the imperfectness of channel state from the transmitter-side has to be taken into consideration in many practical communication systems.
Performance Study of Minimax and Reinforcement Learning Agents Playing the Turn-based Game Iwoki
Published in Applied Artificial Intelligence, 2021
Santiago Videgaín, Pablo García Sánchez
Determinism refers to the intervention of randomness or stochasticity. Thus, a game can be deterministic if there is no room for randomness, or non-deterministic (or stochastic) if randomness intervenes in any of the above characteristics. For example, the HearthStone card game includes random actions after playing some cards (García-Sánchez et al. 2020). The Bayesian Game concept considers as random variables the unknown information of the game. Bayes’ theorem is applied by establishing probabilities about concrete states of the game based on previously known information, after a series of previous actions carried out by the opponents. Bayesian Game, therefore, makes sense for games with imperfect information (Harsanyi 1967).